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Aoyama N, Nakajo K, Sasabe M, Inaba A, Nakanishi Y, Seno H, Yano T. Effects of artificial intelligence assistance on endoscopist performance: Comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video-based models. DEN OPEN 2026; 6:e70083. [PMID: 40322543 PMCID: PMC12046500 DOI: 10.1002/deo2.70083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 01/16/2025] [Accepted: 02/06/2025] [Indexed: 05/08/2025]
Abstract
Objectives Superficial esophageal squamous cell carcinoma (ESCC) detection is crucial. Although narrow-band imaging improves detection, its effectiveness is diminished by inexperienced endoscopists. The effects of artificial intelligence (AI) assistance on ESCC detection by endoscopists remain unclear. Therefore, this study aimed to develop and validate an AI model for ESCC detection using endoscopic video analysis and evaluate diagnostic improvements. Methods Endoscopic videos with and without ESCC lesions were collected from May 2020 to January 2022. The AI model trained on annotated videos and 18 endoscopists (eight experts, 10 non-experts) evaluated their diagnostic performance. After 4 weeks, the endoscopists re-evaluated the test data with AI assistance. Sensitivity, specificity, and accuracy were compared between endoscopists with and without AI assistance. Results Training data comprised 280 cases (140 with and 140 without lesions), and test data, 115 cases (52 with and 63 without lesions). In the test data, the median lesion size was 14.5 mm (range: 1-100 mm), with pathological depths ranging from high-grade intraepithelial to submucosal neoplasia. The model's sensitivity, specificity, and accuracy were 76.0%, 79.4%, and 77.2%, respectively. With AI assistance, endoscopist sensitivity (57.4% vs. 66.5%) and accuracy (68.6% vs. 75.9%) improved significantly, while specificity increased slightly (87.0% vs. 91.6%). Experts demonstrated substantial improvements in sensitivity (59.1% vs. 70.0%) and accuracy (72.1% vs. 79.3%). Non-expert accuracy increased significantly (65.8% vs. 73.3%), with slight improvements in sensitivity (56.1% vs. 63.7%) and specificity (81.9% vs. 89.2%). Conclusions AI assistance enhances ESCC detection and improves endoscopists' diagnostic performance, regardless of experience.
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Affiliation(s)
- Naoki Aoyama
- Department of Gastroenterology and EndoscopyNational Cancer Center Hospital EastChibaJapan
- Department of Gastroenterology and HepatologyKyoto University Graduate School of MedicineKyotoJapan
| | - Keiichiro Nakajo
- Department of Gastroenterology and EndoscopyNational Cancer Center Hospital EastChibaJapan
- NEXT Medical Device Innovation CenterNational Cancer Center Hospital EastChibaJapan
| | - Maasa Sasabe
- Department of Gastroenterology and EndoscopyNational Cancer Center Hospital EastChibaJapan
- Division of EndoscopySaitama Cancer CenterSaitamaJapan
| | - Atsushi Inaba
- Department of Gastroenterology and EndoscopyNational Cancer Center Hospital EastChibaJapan
| | - Yuki Nakanishi
- Department of Gastroenterology and HepatologyKyoto University Graduate School of MedicineKyotoJapan
| | - Hiroshi Seno
- Department of Gastroenterology and HepatologyKyoto University Graduate School of MedicineKyotoJapan
| | - Tomonori Yano
- Department of Gastroenterology and EndoscopyNational Cancer Center Hospital EastChibaJapan
- NEXT Medical Device Innovation CenterNational Cancer Center Hospital EastChibaJapan
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Naskar S, Sharma S, Kuotsu K, Halder S, Pal G, Saha S, Mondal S, Biswas UK, Jana M, Bhattacharjee S. The biomedical applications of artificial intelligence: an overview of decades of research. J Drug Target 2025; 33:717-748. [PMID: 39744873 DOI: 10.1080/1061186x.2024.2448711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Revised: 12/13/2024] [Accepted: 12/26/2024] [Indexed: 01/11/2025]
Abstract
A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.
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Affiliation(s)
- Sweet Naskar
- Department of Pharmaceutics, Institute of Pharmacy, Kalyani, West Bengal, India
| | - Suraj Sharma
- Department of Pharmaceutics, Sikkim Professional College of Pharmaceutical Sciences, Sikkim, India
| | - Ketousetuo Kuotsu
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Suman Halder
- Medical Department, Department of Indian Railway, Kharagpur Division, Kharagpur, West Bengal, India
| | - Goutam Pal
- Service Dispensary, ESI Hospital, Hoogly, West Bengal, India
| | - Subhankar Saha
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India
| | - Shubhadeep Mondal
- Department of Pharmacology, Momtaz Begum Pharmacy College, Rajarhat, West Bengal, India
| | - Ujjwal Kumar Biswas
- School of Pharmaceutical Science (SPS), Siksha O Anusandhan (SOA) University, Bhubaneswar, Odisha, India
| | - Mayukh Jana
- School of Pharmacy, Centurion University of Technology and Management, Centurion University, Bhubaneswar, Odisha, India
| | - Sunirmal Bhattacharjee
- Department of Pharmaceutics, Bharat Pharmaceutical Technology, Amtali, Agartala, Tripura, India
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Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications. Cancer Lett 2025; 614:217555. [PMID: 39952597 DOI: 10.1016/j.canlet.2025.217555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 01/31/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025]
Abstract
With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.
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Affiliation(s)
- Shengyuan Zhou
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yi Xie
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xujiao Feng
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yanyan Li
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Peking University Cancer Hospital and Institute, Beijing, China; Department of Gastrointestinal Cancer, Beijing GoBroad Hospital, Beijing, 102200, China.
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Nathani P, Sharma P. Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach. Gastrointest Endosc Clin N Am 2025; 35:319-353. [PMID: 40021232 DOI: 10.1016/j.giec.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2025]
Abstract
The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.
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Affiliation(s)
- Piyush Nathani
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA.
| | - Prateek Sharma
- Department of Gastroenterology, University of Kansas School of Medicine, Kansas City, KS, USA; Kansas City Veteran Affairs Medical Center, Kansas City, MO, USA
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Nakao E, Yoshio T, Kato Y, Namikawa K, Tokai Y, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Kurihara N, Ishizuka N, Ishihara R, Tada T, Fujisaki J. Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice. Endoscopy 2025; 57:210-217. [PMID: 39317205 DOI: 10.1055/a-2421-3194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect esophageal squamous cell carcinoma (ESCC); however, none of the previous reports were investigations in a clinical setting, being retrospective in design. We therefore conducted this trial to determine how AI can help endoscopists detect ESCC in a clinical setting. METHODS This was a prospective, single-center, exploratory, and randomized controlled trial. High risk patients with ESCC undergoing screening or surveillance esophagogastroduodenoscopy were enrolled and randomly assigned to either the AI or control groups. In the AI group, the endoscopists watched both the AI monitor that detected ESCC with annotation and the normal monitor simultaneously; in the control group, the endoscopists watched only the normal monitor. In both groups, the endoscopists observed the esophagus using white-light imaging (WLI), followed by narrow-band imaging (NBI), then iodine staining. The primary end point was the enhanced detection rate of ESCC by nonexperts using AI. The detection rate was defined as the ratio of WLI/NBI-detected ESCCs to all ESCCs detected by iodine staining. RESULTS 320 patients were included in the analysis. The detection rate of ESCC among nonexperts was 47% in the AI group and 45% in the control group (P = 0.93), with no significant difference, which was similarly found for experts (87% vs. 57%; P = 0.20) and all endoscopists (57% vs. 50%; P = 0.70). CONCLUSIONS This study could not demonstrate an improvement in the esophageal cancer detection rate using the AI diagnostic support system for ESCC.
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Affiliation(s)
- Eisuke Nakao
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Toshiyuki Yoshio
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yusuke Kato
- AI Medical Service, AI Medical Service Inc., Tokyo, Japan
| | - Ken Namikawa
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yoshitaka Tokai
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Shoichi Yoshimizu
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Yusuke Horiuchi
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Akiyoshi Ishiyama
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Nozomi Kurihara
- Clinical Planning and Strategy, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
| | - Naoki Ishizuka
- Clinical Planning and Strategy, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
- Center for Digital Transformation for Health, Kyoto University School of Medicine, Kyoto, Japan
| | - Ryu Ishihara
- Gastrointestinal Oncology, Osaka International Cancer Institute., Osaka, Japan
| | - Tomohiro Tada
- AI Medical Service, AI Medical Service Inc., Tokyo, Japan
- Gastroenterology and Proctology, Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
- Surgical Oncology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Junko Fujisaki
- Gastroenterology, Cancer Institute Hospital, Japanese Foundation For Cancer Research, Tokyo, Japan
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Zhou N, Yuan X, Liu W, Luo Q, Liu R, Hu B. Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions. Chin Med J (Engl) 2025:00029330-990000000-01442. [PMID: 40008787 DOI: 10.1097/cm9.0000000000003490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Indexed: 02/27/2025] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.
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Affiliation(s)
- Nuoya Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xianglei Yuan
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Med-X Center for Materials, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wei Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qi Luo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Ruide Liu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Leon S, Lee S, Perez JE, Hashimoto DA. Artificial intelligence and the education of future surgeons. Am J Surg 2025:116257. [PMID: 39988540 DOI: 10.1016/j.amjsurg.2025.116257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 01/18/2025] [Accepted: 02/18/2025] [Indexed: 02/25/2025]
Abstract
Artificial intelligence (AI) has the potential to reshape surgical education by enabling personalized feedback, advanced competency evaluations, and enhancing resident selection processes. Through AI-driven simulations and real-time feedback systems, surgical trainees can engage in adaptive learning environments that promote deliberate practice and accelerated skill acquisition. Moreover, intraoperative AI tools may soon offer decision support, guiding surgeons during complex procedures. However, integrating AI into surgical education and practice comes with significant challenges. These include the need for high-quality datasets, the transition of AI systems from simulated environments to actual surgeries, and the ethical implications of data privacy, algorithmic bias, and surgeon autonomy. Overreliance on AI could de-skill surgeons, while biased algorithms may perpetuate disparities in resident selection and performance evaluations. To address these issues, regulatory frameworks must be developed to ensure responsible AI use, focusing on transparency, validation, and augmentation rather than replacement of human expertise. Surgeons must decide where AI's use is appropriate, questioning whether capability alone justifies adoption. With careful consideration of these challenges, AI has the potential to revolutionize surgical education and foster a new generation of highly skilled and competent surgeons.
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Affiliation(s)
- Sebastian Leon
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sangjoon Lee
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Juan Esteban Perez
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
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Waki K, Nagaoka K, Okubo K, Kiyama M, Gushima R, Ohno K, Honda M, Yamasaki A, Matsuno K, Furuta Y, Miyamoto H, Naoe H, Amagasaki M, Tanaka Y. Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images. Sci Rep 2025; 15:4003. [PMID: 39893225 PMCID: PMC11787386 DOI: 10.1038/s41598-025-86829-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 01/14/2025] [Indexed: 02/04/2025] Open
Abstract
There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there is currently no artificial intelligence (AI) system that utilizes oral images for ESCC risk assessment. Here, we evaluated three AI models and three fine-tuning approaches with regard to their ESCC predictive power. Our dataset contained 539 cases, which were subdivided into 221 high-risk cases (2491 images) and 318 non-high-risk cases (2524 images). We used 480 cases (4295 images) for the training dataset, and the rest for validation. The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. In addition, when tested with a small number of images containing soft palate data, the model showed high precision: the best AUC model had 0.91 (sensitivity 0.86, specificity 0.79). This study presents a significant advance in the development of an AI-based non-invasive screening tool for the identification of high-risk ESCC patients. The approach may be particularly suitable for institutes with limited medical imaging resources.
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Affiliation(s)
- Kotaro Waki
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Katsuya Nagaoka
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Keishi Okubo
- Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Masato Kiyama
- Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Ryosuke Gushima
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Kento Ohno
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Munenori Honda
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Akira Yamasaki
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Kenshi Matsuno
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Yoki Furuta
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Hideaki Miyamoto
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Hideaki Naoe
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan
| | - Motoki Amagasaki
- Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan
| | - Yasuhito Tanaka
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto, Kumamoto, 860-8556, Japan.
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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Zhang WY, Chang YJ, Shi RH. Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era. World J Gastroenterol 2024; 30:4267-4280. [PMID: 39492825 PMCID: PMC11525855 DOI: 10.3748/wjg.v30.i39.4267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 08/31/2024] [Accepted: 09/19/2024] [Indexed: 10/12/2024] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is the most common histological type of esophageal cancer with a poor prognosis. Early diagnosis and prognosis assessment are crucial for improving the survival rate of ESCC patients. With the advancement of artificial intelligence (AI) technology and the proliferation of medical digital information, AI has demonstrated promising sensitivity and accuracy in assisting precise detection, treatment decision-making, and prognosis assessment of ESCC. It has become a unique opportunity to enhance comprehensive clinical management of ESCC in the era of precision oncology. This review examines how AI is applied to the diagnosis, treatment, and prognosis assessment of ESCC in the era of precision oncology, and analyzes the challenges and potential opportunities that AI faces in clinical translation. Through insights into future prospects, it is hoped that this review will contribute to the real-world application of AI in future clinical settings, ultimately alleviating the disease burden caused by ESCC.
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Affiliation(s)
- Wan-Yue Zhang
- School of Medicine, Southeast University, Nanjing 221000, Jiangsu Province, China
| | - Yong-Jian Chang
- School of Cyber Science and Engineering, Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Rui-Hua Shi
- Department of Gastroenterology, Zhongda Hospital, Southeast University, Nanjing 210009, Jiangsu Province, China
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Theocharopoulos C, Davakis S, Ziogas DC, Theocharopoulos A, Foteinou D, Mylonakis A, Katsaros I, Gogas H, Charalabopoulos A. Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer. Cancers (Basel) 2024; 16:3285. [PMID: 39409906 PMCID: PMC11475041 DOI: 10.3390/cancers16193285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2024] [Revised: 09/21/2024] [Accepted: 09/24/2024] [Indexed: 10/20/2024] Open
Abstract
Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods for image analysis constitutes a promising adjunct for the clinical endoscopist that could effectively decrease BE overdiagnosis and unnecessary surveillance, while also assisting in the timely detection of dysplastic BE and esophageal cancer. A plethora of studies published during the last five years have consistently reported highly accurate DL algorithms with comparable or superior performance compared to endoscopists. Recent efforts aim to expand DL utilization into further aspects of esophageal neoplasia management including histologic diagnosis, segmentation of gross tumor volume, pretreatment prediction and post-treatment evaluation of patient response to systemic therapy and operative guidance during minimally invasive esophagectomy. Our manuscript serves as an introduction to the growing literature of DL applications for image analysis in the management of esophageal neoplasia, concisely presenting all currently published studies. We also aim to guide the clinician across basic functional principles, evaluation metrics and limitations of DL for image recognition to facilitate the comprehension and critical evaluation of the presented studies.
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Affiliation(s)
| | - Spyridon Davakis
- First Department of Surgery, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.D.); (A.M.); (I.K.); (A.C.)
| | - Dimitrios C. Ziogas
- First Department of Medicine, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.C.Z.); (D.F.); (H.G.)
| | - Achilleas Theocharopoulos
- Department of Electrical and Computer Engineering, National Technical University of Athens, 10682 Athens, Greece;
| | - Dimitra Foteinou
- First Department of Medicine, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.C.Z.); (D.F.); (H.G.)
| | - Adam Mylonakis
- First Department of Surgery, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.D.); (A.M.); (I.K.); (A.C.)
| | - Ioannis Katsaros
- First Department of Surgery, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.D.); (A.M.); (I.K.); (A.C.)
| | - Helen Gogas
- First Department of Medicine, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (D.C.Z.); (D.F.); (H.G.)
| | - Alexandros Charalabopoulos
- First Department of Surgery, School of Medicine, Laiko General Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (S.D.); (A.M.); (I.K.); (A.C.)
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Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5. [DOI: 10.35713/aic.v5.i2.97317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
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Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
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13
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Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion 2024; 105:419-435. [PMID: 39068926 DOI: 10.1159/000540251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated. SUMMARY In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation. KEY MESSAGES Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.
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Affiliation(s)
- Ryosuke Kikuchi
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuaki Okamoto
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ozawa
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
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14
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Zhang L, Yao L, Lu Z, Yu H. Current status of quality control in screening esophagogastroduodenoscopy and the emerging role of artificial intelligence. Dig Endosc 2024; 36:5-15. [PMID: 37522555 DOI: 10.1111/den.14649] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/27/2023] [Indexed: 08/01/2023]
Abstract
Esophagogastroduodenoscopy (EGD) screening is being implemented in countries with a high incidence of upper gastrointestinal (UGI) cancer. High-quality EGD screening ensures the yield of early diagnosis and prevents suffering from advanced UGI cancer and minimal operational-related discomfort. However, performance varied dramatically among endoscopists, and quality control for EGD screening remains suboptimal. Guidelines have recommended potential measures for endoscopy quality improvement and research has been conducted for evidence. Moreover, artificial intelligence offers a promising solution for computer-aided diagnosis and quality control during EGD examinations. In this review, we summarized the key points for quality assurance in EGD screening based on current guidelines and evidence. We also outline the latest evidence, limitations, and future prospects of the emerging role of artificial intelligence in EGD quality control, aiming to provide a foundation for improving the quality of EGD screening.
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Affiliation(s)
- Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zihua Lu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
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15
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Kim DK, Kim BS, Kim YJ, Kim S, Yoon D, Lee DK, Jeong J, Jo YH. Development and validation of an artificial intelligence algorithm for detecting vocal cords in video laryngoscopy. Medicine (Baltimore) 2023; 102:e36761. [PMID: 38134083 PMCID: PMC10735139 DOI: 10.1097/md.0000000000036761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
Airway procedures in life-threatening situations are vital for saving lives. Video laryngoscopy (VL) is commonly performed during endotracheal intubation (ETI) in the emergency department. Artificial intelligence (AI) is widely used in the medical field, particularly to detect anatomical structures. This study aimed to develop an AI algorithm that detects vocal cords from VL images acquired during emergent situations. This retrospective study used VL images acquired in the emergency department to facilitate the ETI. The vocal cord image was labeled with a ground-truth bounding box. The dataset was divided into training and validation datasets. The algorithm was developed from a training dataset using the YOLOv4 model. The performance of the algorithm was evaluated using a test set. The test set was further divided into specific environments during the ETI for clinical subgroup analysis. In total, 20,161 images from 84 patients were used in this study. A total of 10,287, 5766, and 4108 images were used for the model training, validation, and test sets, respectively. The developed algorithm achieved F1 score 0.906, sensitivity 0.963, and specificity 0.842 in the validation set. The performance in the test set was F1 score 0.808, sensitivity 0.823, and specificity 0.804. We developed and validated an AI algorithm to detect vocal cords in VL. This algorithm demonstrated a high performance. The algorithm can be used to determine the vocal cord to ensure safe ETI.
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Affiliation(s)
- Dae Kon Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Seoul National University, College of Medicine, Seoul, Republic of Korea
| | - Byeong Soo Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea
| | - Yu Jin Kim
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Seoul National University, College of Medicine, Seoul, Republic of Korea
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Dan Yoon
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea
| | - Dong Keon Lee
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Seoul National University, College of Medicine, Seoul, Republic of Korea
| | - Joo Jeong
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Seoul National University, College of Medicine, Seoul, Republic of Korea
| | - You Hwan Jo
- Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Seoul National University, College of Medicine, Seoul, Republic of Korea
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16
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Alghazo J, Latif G. AI/ML-Based Medical Image Processing and Analysis. Diagnostics (Basel) 2023; 13:3671. [PMID: 38132255 PMCID: PMC10742629 DOI: 10.3390/diagnostics13243671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
The medical field is experiencing remarkable advancements, notably with the latest technologies-artificial intelligence (AI), big data, high-performance computing (HPC), and high-throughput computing (HTC)-that are in place to offer groundbreaking solutions to support medical professionals in the diagnostic process [...].
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Affiliation(s)
- Jaafar Alghazo
- Artificial Intelligence Research Initiative, College of Engineering and Mines, University of North Dakota, Grand Forks, ND 58202, USA;
| | - Ghazanfar Latif
- Computer Science Department, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
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17
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Yuan X, Zeng X, He L, Ye L, Liu W, Hu Y, Hu B. Artificial intelligence for detecting and delineating a small flat-type early esophageal squamous cell carcinoma under multimodal imaging. Endoscopy 2023; 55:E141-E142. [PMID: 36307086 PMCID: PMC9829824 DOI: 10.1055/a-1956-0569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Xianhui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Long He
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Liansong Ye
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yanxing Hu
- Xiamen Innovision Medical Technology Co., Ltd., Xiamen, Fujian Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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18
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Guidozzi N, Menon N, Chidambaram S, Markar SR. The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis. Dis Esophagus 2023; 36:doad048. [PMID: 37480192 PMCID: PMC10789250 DOI: 10.1093/dote/doad048] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.
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Affiliation(s)
- Nadia Guidozzi
- Department of General Surgery, University of Witwatersrand, Johannesburg, South Africa
| | - Nainika Menon
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
| | - Swathikan Chidambaram
- Academic Surgical Unit, Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London, UK
| | - Sheraz Rehan Markar
- Department of General Surgery, Oxford University Hospitals, Oxford, UK
- Nuffield Department of Surgery, University of Oxford, Oxford, UK
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19
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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20
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Hu Y, Chen Y, Qin Y, Huang R. Learning entity-oriented representation for biomedical relation extraction. J Biomed Inform 2023; 147:104527. [PMID: 37852347 DOI: 10.1016/j.jbi.2023.104527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 10/20/2023]
Abstract
Biomedical Relation Extraction (BioRE) aims to automatically extract semantic relations for given entity pairs and is of great significance in biomedical research. Current popular methods often utilize pretrained language models to extract semantic features from individual input instances, which frequently suffer from overlapping semantics. Overlapping semantics refers to the situation in which a sentence contains multiple entity pairs that share the same context, leading to highly similar information between these entity pairs. In this study, we propose a model for learning Entity-oriented Representation (EoR) that aims to improve the performance of the model by enhancing the discriminability between entity pairs. It contains three modules: sentence representation, entity-oriented representation, and output. The first module learns the global semantic information of the input instance; the second module focuses on extracting the semantic information of the sentence from the target entities; and the third module enhances distinguishability among entity pairs and classifies the relation type. We evaluated our approach on four BioRE tasks with eight datasets, and the experiments showed that our EoR achieved state-of-the-art performance for PPI, DDI, CPI, and DPI tasks. Further analysis demonstrated the benefits of entity-oriented semantic information in handling multiple entity pairs in the BioRE task.
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Affiliation(s)
- Ying Hu
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yanping Chen
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Yongbin Qin
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
| | - Ruizhang Huang
- Text Computing and Cognitive Intelligence Engineering Research Center of National Education Ministry, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
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21
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Tani Y, Ishihara R, Inoue T, Okubo Y, Kawakami Y, Matsueda K, Miyake M, Yoshii S, Shichijo S, Kanesaka T, Yamamoto S, Takeuchi Y, Higashino K, Uedo N, Michida T, Kato Y, Tada T. A single-center prospective study evaluating the usefulness of artificial intelligence for the diagnosis of esophageal squamous cell carcinoma in a real-time setting. BMC Gastroenterol 2023; 23:184. [PMID: 37231330 PMCID: PMC10210292 DOI: 10.1186/s12876-023-02788-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/26/2023] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Several pre-clinical studies have reported the usefulness of artificial intelligence (AI) systems in the diagnosis of esophageal squamous cell carcinoma (ESCC). We conducted this study to evaluate the usefulness of an AI system for real-time diagnosis of ESCC in a clinical setting. METHODS This study followed a single-center prospective single-arm non-inferiority design. Patients at high risk for ESCC were recruited and real-time diagnosis by the AI system was compared with that of endoscopists for lesions suspected to be ESCC. The primary outcomes were the diagnostic accuracy of the AI system and endoscopists. The secondary outcomes were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and adverse events. RESULTS A total of 237 lesions were evaluated. The accuracy, sensitivity, and specificity of the AI system were 80.6%, 68.2%, and 83.4%, respectively. The accuracy, sensitivity, and specificity of endoscopists were 85.7%, 61.4%, and 91.2%, respectively. The difference between the accuracy of the AI system and that of the endoscopists was - 5.1%, and the lower limit of the 90% confidence interval was less than the non-inferiority margin. CONCLUSIONS The non-inferiority of the AI system in comparison with endoscopists in the real-time diagnosis of ESCC in a clinical setting was not proven. TRIAL REGISTRATION Japan Registry of Clinical Trials (jRCTs052200015, 18/05/2020).
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Affiliation(s)
- Yasuhiro Tani
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan.
| | - Takahiro Inoue
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Yuki Okubo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Yushi Kawakami
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Katsunori Matsueda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Muneaki Miyake
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Shunsuke Yoshii
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Satoki Shichijo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Takashi Kanesaka
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Sachiko Yamamoto
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Yoji Takeuchi
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Koji Higashino
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Noriya Uedo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Tomoki Michida
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
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22
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Pan Y, He L, Chen W, Yang Y. The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma. Front Oncol 2023; 13:1198941. [PMID: 37293591 PMCID: PMC10247226 DOI: 10.3389/fonc.2023.1198941] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/16/2023] [Indexed: 06/10/2023] Open
Abstract
Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract. The most effective method of reducing the disease burden in areas with a high incidence of esophageal cancer is to prevent the disease from developing into invasive cancer through screening. Endoscopic screening is key for the early diagnosis and treatment of ESCC. However, due to the uneven professional level of endoscopists, there are still many missed cases because of failure to recognize lesions. In recent years, along with remarkable progress in medical imaging and video evaluation technology based on deep machine learning, the development of artificial intelligence (AI) is expected to provide new auxiliary methods of endoscopic diagnosis and the treatment of early ESCC. The convolution neural network (CNN) in the deep learning model extracts the key features of the input image data using continuous convolution layers and then classifies images through full-layer connections. The CNN is widely used in medical image classification, and greatly improves the accuracy of endoscopic image classification. This review focuses on the AI-assisted diagnosis of early ESCC and prediction of early ESCC invasion depth under multiple imaging modalities. The excellent image recognition ability of AI is suitable for the detection and diagnosis of ESCC and can reduce missed diagnoses and help endoscopists better complete endoscopic examinations. However, the selective bias used in the training dataset of the AI system affects its general utility.
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Affiliation(s)
- Yuwei Pan
- Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
| | - Lanying He
- Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
| | - Weiqing Chen
- Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
| | - Yongtao Yang
- Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China
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23
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Feng Y, Liang Y, Li P, Long Q, Song J, Li M, Wang X, Cheng CE, Zhao K, Ma J, Zhao L. Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system. Discov Oncol 2023; 14:73. [PMID: 37208546 DOI: 10.1007/s12672-023-00694-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/15/2023] [Indexed: 05/21/2023] Open
Abstract
BACKGROUND The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform. METHODS In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis. RESULTS In the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006). CONCLUSIONS The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.
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Affiliation(s)
- Yadong Feng
- Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China.
- Department of Gastroenterology, the Affiliated Changshu Hospital of Nantong University, Changshu No. 2 People's Hospital, 18 Taishan Road, Suzhou, 215500, China.
| | - Yan Liang
- Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China
| | - Peng Li
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China
| | - Qigang Long
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China
| | - Jie Song
- Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China
| | - Mengjie Li
- Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China
| | - Xiaofen Wang
- Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China
| | - Cui-E Cheng
- Department of Gastroenterology, the Affiliated Changshu Hospital of Nantong University, Changshu No. 2 People's Hospital, 18 Taishan Road, Suzhou, 215500, China
| | - Kai Zhao
- Department of Gastroenterology, Changzhou Jintan First People's Hospital Affiliated to Jiangsu University, 500 Jintan Avenue, Jintan, 210036, China
| | - Jifeng Ma
- Department of Gastroenterology, General Global Maanshan 17th Metallurgy Hospital, 828 West Hunan Road, Maanshan, 243011, China
| | - Lingxiao Zhao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 96 Jinzhai Road, Hefei, 230026, China.
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Yuan XL, Zeng XH, Liu W, Mou Y, Zhang WH, Zhou ZD, Chen X, Hu YX, Hu B. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest Endosc 2023; 97:664-672.e4. [PMID: 36509114 DOI: 10.1016/j.gie.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 11/04/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND AIMS Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. METHODS Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. RESULTS The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. CONCLUSIONS The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xian-Hui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yi Mou
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wan-Hong Zhang
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, Sichuan, China
| | - Zheng-Duan Zhou
- Department of Gastroenterology, Zigong Fourth People's Hospital, Zigong, Sichuan, China
| | - Xin Chen
- The First People's Hospital of Shuangliu District, Chengdu, Sichuan, China
| | - Yan-Xing Hu
- Xiamen Innovision Medical Technology Co, Ltd, Xiamen, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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25
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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26
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Islam MM, Poly TN, Walther BA, Yeh CY, Seyed-Abdul S, Li YC(J, Lin MC. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14235996. [PMID: 36497480 PMCID: PMC9736434 DOI: 10.3390/cancers14235996] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Bruno Andreas Walther
- Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany
| | - Chih-Yang Yeh
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Seyed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence:
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27
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Jin J, Zhang Q, Dong B, Ma T, Mei X, Wang X, Song S, Peng J, Wu A, Dong L, Kong D. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video). Front Oncol 2022; 12:927868. [PMID: 36338757 PMCID: PMC9630732 DOI: 10.3389/fonc.2022.927868] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/05/2022] [Indexed: 12/04/2022] Open
Abstract
The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ2 = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ2 = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ2 = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ2 = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ2 = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qianqian Zhang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bill Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Tao Ma
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Xuecan Mei
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xi Wang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shaofang Song
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Jie Peng
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Aijiu Wu
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Lanfang Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Derun Kong
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Derun Kong,
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Kumagai Y, Takubo K, Kawada K, Ohue M, Higashi M, Ishiguro T, Hatano S, Toyomasu Y, Matsuyama T, Mochiki E, Ishida H. Endocytoscopic Observation of Esophageal Lesions: Our Own Experience and a Review of the Literature. Diagnostics (Basel) 2022; 12:2222. [PMID: 36140623 PMCID: PMC9498282 DOI: 10.3390/diagnostics12092222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/02/2022] [Accepted: 09/12/2022] [Indexed: 11/17/2022] Open
Abstract
This review outlines the process of the development of the endocytoscope (EC) with reference to previously reported studies including our own. The EC is an ultra-high-magnification endoscope capable of imaging at the cellular level. The esophagus is the most suitable site for EC observation because it is amenable to vital staining. The diagnosis of esophageal lesions using EC is based on nuclear density and nuclear abnormality, allowing biopsy histology to be omitted. The observation of nuclear abnormality requires a magnification of ×600 or higher using digital technology. Several staining methods have been proposed, but single staining with toluidine blue or methylene blue is most suitable because the contrast at the border of a cancerous area can be easily identified. A three-tier classification of esophageal lesions visualized by EC is proposed: Type 1 (non-cancerous), Type 2 (endocytoscopic borderline), and Type 3 (cancerous). Since characteristic EC images reflecting pathology can be obtained from non-cancerous esophageal lesions, a modified form of classification with four additional characteristic non-cancerous EC features has also been proposed. Recently, deep-learning AI for analysis of esophageal EC images has revealed that its diagnostic accuracy is comparable to that of expert pathologists.
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Affiliation(s)
- Youichi Kumagai
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo 173-0015, Japan
| | - Kenro Kawada
- Department of Esophageal and General Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Masayuki Ohue
- Department of Surgery, Osaka International Cancer Center, Osaka 541-8567, Japan
| | - Morihiro Higashi
- Department of Pathology, Saitama Medical Center, Saitama Medical University, Saitama 350-0495, Japan
| | - Toru Ishiguro
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Satoshi Hatano
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Yoshitaka Toyomasu
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Takatoshi Matsuyama
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Erito Mochiki
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
| | - Hideyuki Ishida
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Kawagoe 350-8550, Saitama, Japan
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Kumagai Y, Takubo K, Sato T, Ishikawa H, Yamamoto E, Ishiguro T, Hatano S, Toyomasu Y, Kawada K, Matsuyama T, Mochiki E, Ishida H, Tada T. AI analysis and modified type classification for endocytoscopic observation of esophageal lesions. Dis Esophagus 2022; 35:6548110. [PMID: 35292794 DOI: 10.1093/dote/doac010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/06/2021] [Accepted: 02/07/2022] [Indexed: 12/11/2022]
Abstract
Endocytoscopy (EC) facilitates real-time histological diagnosis of esophageal lesions in vivo. We developed a deep-learning artificial intelligence (AI) system for analysis of EC images and compared its diagnostic ability with that of an expert pathologist and nonexpert endoscopists. Our new AI was based on a vision transformer model (DeiT) and trained using 7983 EC images of the esophagus (2368 malignant and 5615 nonmalignant). The AI evaluated 114 randomly arranged EC pictures (33 ESCC and 81 nonmalignant lesions) from 38 consecutive cases. An expert pathologist and two nonexpert endoscopists also analyzed the same image set according to the modified type classification (adding four EC features of nonmalignant lesions to our previous classification). The area under the curve calculated from the receiver-operating characteristic curve for the AI analysis was 0.92. In per-image analysis, the overall accuracy of the AI, pathologist, and two endoscopists was 91.2%, 91.2%, 85.9%, and 83.3%, respectively. The kappa value between the pathologist and the AI, and between the two endoscopists and the AI showed moderate concordance; that between the pathologist and the two endoscopists showed poor concordance. In per-patient analysis, the overall accuracy of the AI, pathologist, and two endoscopists was 94.7%, 92.1%, 86.8%, and 89.5%, respectively. The modified type classification aided high overall diagnostic accuracy by the pathologist and nonexpert endoscopists. The diagnostic ability of the AI was equal or superior to that of the experienced pathologist. AI is expected to support endoscopists in diagnosing esophageal lesions based on EC images.
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Affiliation(s)
- Youichi Kumagai
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Taku Sato
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Hiroyasu Ishikawa
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Eisuke Yamamoto
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Toru Ishiguro
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Satoshi Hatano
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Yoshitaka Toyomasu
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Kenro Kawada
- Department of Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takatoshi Matsuyama
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Erito Mochiki
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Hideyuki Ishida
- Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
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30
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Tu JX, Lin XT, Ye HQ, Yang SL, Deng LF, Zhu RL, Wu L, Zhang XQ. Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer. Front Oncol 2022; 12:972357. [PMID: 36091151 PMCID: PMC9453500 DOI: 10.3389/fonc.2022.972357] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 07/29/2022] [Indexed: 12/09/2022] Open
Abstract
ObjectiveUsing visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.MethodsOn April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.ResultsA total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.ConclusionsAn increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.
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Affiliation(s)
- Jia-xin Tu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Xue-ting Lin
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Hui-qing Ye
- School of Public Health, Nanchang University, Nanchang, China
| | - Shan-lan Yang
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Li-fang Deng
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Ruo-ling Zhu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
| | - Lei Wu
- School of Public Health, Nanchang University, Nanchang, China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
| | - Xiao-qiang Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- *Correspondence: Lei Wu, ; Xiao-qiang Zhang,
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Fang YJ, Mukundan A, Tsao YM, Huang CW, Wang HC. Identification of Early Esophageal Cancer by Semantic Segmentation. J Pers Med 2022; 12:1204. [PMID: 35893299 PMCID: PMC9331549 DOI: 10.3390/jpm12081204] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/19/2022] [Accepted: 07/22/2022] [Indexed: 02/06/2023] Open
Abstract
Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder-decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study.
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Affiliation(s)
- Yu-Jen Fang
- Department of Internal Medicine, National Taiwan University Hospital, Yun-Lin Branch, No. 579, Sec. 2, Yunlin Rd., Dou-Liu 64041, Taiwan;
- Department of Internal Medicine, National Taiwan University College of Medicine, No. 1 Jen Ai Rd. Sec. 1, Taipei 10051, Taiwan
| | - Arvind Mukundan
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan; (A.M.); (Y.-M.T.)
| | - Yu-Ming Tsao
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan; (A.M.); (Y.-M.T.)
- Hitspectra Intelligent Technology Co., Ltd., 4F., No. 2, Fuxing 4th Rd., Qianzhen Dist., Kaohsiung 80661, Taiwan
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya Dist., Kaohsiung 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung 90741, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High Tech Innovations (AIM-HI), Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chiayi 62102, Taiwan; (A.M.); (Y.-M.T.)
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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Minchenberg SB, Walradt T, Glissen Brown JR. Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy. World J Gastrointest Oncol 2022; 14:989-1001. [PMID: 35646286 PMCID: PMC9124983 DOI: 10.4251/wjgo.v14.i5.989] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/21/2021] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.
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Affiliation(s)
- Scott B Minchenberg
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Trent Walradt
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
| | - Jeremy R Glissen Brown
- Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Tajiri A, Ishihara R, Kato Y, Inoue T, Matsueda K, Miyake M, Waki K, Shimamoto Y, Fukuda H, Matsuura N, Egawa S, Yamaguchi S, Ogiyama H, Ogiso K, Nishida T, Aoi K, Tada T. Utility of an artificial intelligence system for classification of esophageal lesions when simulating its clinical use. Sci Rep 2022; 12:6677. [PMID: 35461350 PMCID: PMC9035159 DOI: 10.1038/s41598-022-10739-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 09/29/2021] [Indexed: 12/04/2022] Open
Abstract
Previous reports have shown favorable performance of artificial intelligence (AI) systems for diagnosing esophageal squamous cell carcinoma (ESCC) compared with endoscopists. However, these findings don't reflect performance in clinical situations, as endoscopists classify lesions based on both magnified and non-magnified videos, while AI systems often use only a few magnified narrow band imaging (NBI) still images. We evaluated the performance of the AI system in simulated clinical situations. We used 25,048 images from 1433 superficial ESCC and 4746 images from 410 noncancerous esophagi to construct our AI system. For the validation dataset, we took NBI videos of suspected superficial ESCCs. The AI system diagnosis used one magnified still image taken from each video, while 19 endoscopists used whole videos. We used 147 videos and still images including 83 superficial ESCC and 64 non-ESCC lesions. The accuracy, sensitivity and specificity for the classification of ESCC were, respectively, 80.9% [95% CI 73.6-87.0], 85.5% [76.1-92.3], and 75.0% [62.6-85.0] for the AI system and 69.2% [66.4-72.1], 67.5% [61.4-73.6], and 71.5% [61.9-81.0] for the endoscopists. The AI system correctly classified all ESCCs invading the muscularis mucosa or submucosa and 96.8% of lesions ≥ 20 mm, whereas even the experts diagnosed some of them as non-ESCCs. Our AI system showed higher accuracy for classifying ESCC and non-ESCC than endoscopists. It may provide valuable diagnostic support to endoscopists.
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Affiliation(s)
- Ayaka Tajiri
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan.
| | | | - Takahiro Inoue
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Katsunori Matsueda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Muneaki Miyake
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Kotaro Waki
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Yusaku Shimamoto
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
| | - Noriko Matsuura
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan
- Department of Gastroenterology, Keio University Hospital, Tokyo, Japan
| | - Satoshi Egawa
- Department of Gastroenterology, Osaka Police Hospital, Osaka, Japan
| | | | - Hideharu Ogiyama
- Departments of Gastroenterology and Hepatology, Itami City Hospital, Osaka, Japan
| | - Kiyoshi Ogiso
- Department of Gastroenterology, JR Osaka Railway Hospital, Osaka, Japan
| | - Tsutomu Nishida
- Department of Gastroenterology, Toyonaka Municipal Hospital, Osaka, Japan
| | - Kenji Aoi
- Department of Gastroenterology, Kaizuka City Hospital, Osaka, Japan
| | - Tomohiro Tada
- AI Medical Service Inc, Tokyo, Japan
- Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Ishihara R, Muto M. Current status of endoscopic detection, characterization and staging of superficial esophageal squamous cell carcinoma. Jpn J Clin Oncol 2022; 52:799-805. [PMID: 35452124 DOI: 10.1093/jjco/hyac064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND This review focuses on the current status of endoscopic detection, characterization and tumour category staging of oesophagealsquamous cell carcinoma. DETECTION The diagnostic yield of white-light imaging is limited and narrow-band imaging has demonstrated a better performance for detecting oesophageal cancer. Narrow-band imaging has also shown similar sensitivity and superior specificity to iodine staining. CHARACTERIZATION Accurate differentiation between cancerous and non-cancerous lesions can be achieved by magnifying narrow-band imaging or iodine staining with confirmation of a pink-colour sign. A per-patient analysis of a randomized study showed similar sensitivities, specificities and overall accuracies of magnifying narrow-band imaging and iodine staining of 82.2%, 95.1% and 91.2%, and 80.5%, 94.3% and 90.5%, respectively. TUMOUR-STAGING The diagnostic capability of endoscopic ultrasonography after conventional and narrow-band imaging in terms of tumour depth was evaluated in a multicentre prospective study. Endoscopic ultrasonography did not significantly improve the accuracy for distinguishing between mucosal or submucosal microinvasive cancer and deeper cancers from 72.9 to 74.0%, suggesting that additional endoscopic ultrasonography did not improve the diagnostic accuracy. In addition, endoscopic ultrasonography increased the incidence of overdiagnosis, defined as a diagnosis of cancer depth greater than the actual depth, by 6.6%. The risk of overdiagnosis by endoscopic ultrasonography was reconfirmed in two systematic reviews. CONCLUSIONS Narrow-band imaging is currently considered as the standard modality for the detection and characterization of oesophageal cancer. The risk of overdiagnosis should be considered when applying endoscopic ultrasonography for the evaluation of tumour invasion depth of superficial oesophageal squamous cell carcinoma.
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Affiliation(s)
- Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Manabu Muto
- Department of Therapeutic Oncology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Frazzoni L, Arribas J, Antonelli G, Libanio D, Ebigbo A, van der Sommen F, de Groof AJ, Fukuda H, Ohmori M, Ishihara R, Wu L, Yu H, Mori Y, Repici A, Bergman JJGHM, Sharma P, Messmann H, Hassan C, Fuccio L, Dinis-Ribeiro M. Endoscopists' diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studies. Endoscopy 2022; 54:403-411. [PMID: 33951743 DOI: 10.1055/a-1500-3730] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Estimates on miss rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper GI endoscopy are not fully established owing to the lack of infrastructure to measure endoscopists' competence. We aimed to assess endoscopists' accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies. METHODS Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC). RESULTS Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists' sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %-84 %) and 79 % (95 %CI 76 %-81 %), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93-0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88-0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84-0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %-89 %] vs. 75 % [95 %CI 72 %-78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %-87 %] vs. 71 % [95 %CI 67 %-75 %]). CONCLUSION We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.
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Affiliation(s)
- Leonardo Frazzoni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Julia Arribas
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
- Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Diogo Libanio
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayasu Ohmori
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Alessandro Repici
- Digestive Endoscopy Unit, Humanitas Research Hospital - IRCCS, Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Mário Dinis-Ribeiro
- Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal
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Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022; 2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]
Abstract
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image-enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.
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Affiliation(s)
- Sayaka Nagao
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
- Department of Endoscopy and Endoscopic SurgeryGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Yasuhiro Tani
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Junichi Shibata
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | - Yosuke Tsuji
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
- AI Medical Service Inc.TokyoJapan
- Department of Surgical OncologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Ryu Ishihara
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Mitsuhiro Fujishiro
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
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Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022; 55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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Liu W, Yuan X, Guo L, Pan F, Wu C, Sun Z, Tian F, Yuan C, Zhang W, Bai S, Feng J, Hu Y, Hu B. Artificial Intelligence for Detecting and Delineating Margins of Early ESCC Under WLI Endoscopy. Clin Transl Gastroenterol 2022; 13:e00433. [PMID: 35130184 PMCID: PMC8806389 DOI: 10.14309/ctg.0000000000000433] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/13/2021] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Conventional white light imaging (WLI) endoscopy is the most common screening technique used for detecting early esophageal squamous cell carcinoma (ESCC). Nevertheless, it is difficult to detect and delineate margins of early ESCC using WLI endoscopy. This study aimed to develop an artificial intelligence (AI) model to detect and delineate margins of early ESCC under WLI endoscopy. METHODS A total of 13,083 WLI images from 1,239 patients were used to train and test the AI model. To evaluate the detection performance of the model, 1,479 images and 563 images were used as internal and external validation data sets, respectively. For assessing the delineation performance of the model, 1,114 images and 211 images were used as internal and external validation data sets, respectively. In addition, 216 images were used to compare the delineation performance between the model and endoscopists. RESULTS The model showed an accuracy of 85.7% and 84.5% in detecting lesions in internal and external validation, respectively. For delineating margins, the model achieved an accuracy of 93.4% and 95.7% in the internal and external validation, respectively, under an overlap ratio of 0.60. The accuracy of the model, senior endoscopists, and expert endoscopists in delineating margins were 98.1%, 78.6%, and 95.3%, respectively. The proposed model achieved similar delineating performance compared with that of expert endoscopists but superior to senior endoscopists. DISCUSSION We successfully developed an AI model, which can be used to accurately detect early ESCC and delineate the margins of the lesions under WLI endoscopy.
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Affiliation(s)
- Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Linjie Guo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Feng Pan
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, Jiangsu, China;
| | - Chuncheng Wu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhongshang Sun
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, Jiangsu, China;
| | - Feng Tian
- Department of Gastroenterology, Zigong Fourth People's Hospital, Zigong, Sichuan, China;
| | - Cong Yuan
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China;
| | - Wanhong Zhang
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, Sichuan, China;
| | - Shuai Bai
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Feng
- Xiamen Innovision Medical Technology Co, Ltd, Xiamen, Fujian, China.
| | - Yanxing Hu
- Xiamen Innovision Medical Technology Co, Ltd, Xiamen, Fujian, China.
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Yuan XL, Guo LJ, Liu W, Zeng XH, Mou Y, Bai S, Pan ZG, Zhang T, Pu WF, Wen C, Wang J, Zhou ZD, Feng J, Hu B. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: A multicenter study. J Gastroenterol Hepatol 2022; 37:169-178. [PMID: 34532890 DOI: 10.1111/jgh.15689] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/09/2021] [Accepted: 09/11/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIM Diagnosis of esophageal squamous cell carcinoma (ESCC) is complicated and requires substantial expertise and experience. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC under multiple endoscopic imaging modalities. METHODS Endoscopic images were retrospectively collected from West China Hospital, Sichuan University as a training dataset and an independent internal validation dataset. Images from other four hospitals were used as an external validation dataset. The AI system was compared with 11 experienced endoscopists. Furthermore, videos were collected to assess the performance of the AI system. RESULTS A total of 53 933 images from 2621 patients and 142 videos from 19 patients were used to develop and validate the AI system. In the internal and external validation datasets, the performance of the AI system under all or different endoscopic imaging modalities was satisfactory, with sensitivity of 92.5-99.7%, specificity of 78.5-89.0%, and area under the receiver operating characteristic curves of 0.906-0.989. The AI system achieved comparable performance with experienced endoscopists. Regarding superficial ESCC confined to the epithelium, the AI system was more sensitive than experienced endoscopists on white-light imaging (90.8% vs 82.5%, P = 0.022). Moreover, the AI system exhibited good performance in videos, with sensitivity of 89.5-100% and specificity of 73.7-89.5%. CONCLUSIONS We developed an AI system that showed comparable performance with experienced endoscopists in detecting superficial ESCC under multiple endoscopic imaging modalities and might provide valuable support for inexperienced endoscopists, despite requiring further evaluation.
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Affiliation(s)
- Xiang-Lei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Lin-Jie Guo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Xian-Hui Zeng
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Mou
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Shuai Bai
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhen-Guo Pan
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, China
| | - Tao Zhang
- Department of Gastroenterology, Nanchong Central Hospital, Nanchong, China
| | - Wen-Feng Pu
- Department of Gastroenterology, Nanchong Central Hospital, Nanchong, China
| | - Chun Wen
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, China
| | - Jun Wang
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Zheng-Duan Zhou
- Department of Gastroenterology, Zigong Fourth People's Hospital, Zigong, China
| | - Jing Feng
- Xiamen Innovision Medical Technology Co., Ltd., Xiamen, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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Li Q, Liu BR. Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer. Shijie Huaren Xiaohua Zazhi 2021; 29:1389-1395. [DOI: 10.11569/wcjd.v29.i24.1389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.
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Affiliation(s)
- Qing Li
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
| | - Bing-Rong Liu
- Department of Gastroenterology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, Henan Province, China
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Yang XX, Li Z, Shao XJ, Ji R, Qu JY, Zheng MQ, Sun YN, Zhou RC, You H, Li LX, Feng J, Yang XY, Li YQ, Zuo XL. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig Endosc 2021; 33:1075-1084. [PMID: 33275789 DOI: 10.1111/den.13908] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Endoscopic diagnosis of early esophageal squamous cell cancer (ESCC) is complicated and dependent on operators' experience. This study aimed to develop an artificial intelligence (AI) model for automatic diagnosis of early ESCC. METHODS Non-magnifying and magnifying endoscopic images of normal/noncancerous lesions, early ESCC, and advanced esophageal cancer (AEC) were retrospectively obtained from Qilu Hospital of Shandong University. A total of 10,988 images from 5075 cases were chosen for training and validation. Another 2309 images from 1055 cases were collected for testing. One hundred and four real-time videos were also collected to evaluate the diagnostic performance of the AI model. The diagnostic performance of the AI model was compared with endoscopists by magnifying images and the assistant efficiency of the AI model for novices was evaluated. RESULTS The AI diagnosis for non-magnifying images showed a per-patient accuracy, sensitivity, and specificity of 99.5%, 100%, 99.5% for white light imaging, and 97.0%, 97.2%, 96.4% for optical enhancement/iodine straining images. Regarding diagnosis for magnifying images, the per-patient accuracy, sensitivity, and specificity were 88.1%, 90.9%, and 85.0%. The diagnostic accuracy of the AI model was similar to experts (84.5%, P = 0.205) and superior to novices (68.5%, P = 0.005). The diagnostic performance of novices was significantly improved by AI assistance. When it comes to the diagnosis for real-time videos, the AI model showed acceptable performance as well. CONCLUSIONS The AI model could accurately recognize early ESCC among noncancerous mucosa and AEC. It could be a potential assistant for endoscopists, especially for novices.
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Affiliation(s)
- Xiao-Xiao Yang
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xue-Jun Shao
- Qingdao Medicon Digital Engineering Co. Ltd, Qingdao, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI tumor, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jun-Yan Qu
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Meng-Qi Zheng
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi-Ning Sun
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ru-Chen Zhou
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hang You
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Li-Xiang Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jian Feng
- Qingdao Medicon Digital Engineering Co. Ltd, Qingdao, China
| | - Xiao-Yun Yang
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yan-Qing Li
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Laboratory of Translational Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiu-Li Zuo
- Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI tumor, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
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Waki K, Ishihara R, Kato Y, Shoji A, Inoue T, Matsueda K, Miyake M, Shimamoto Y, Fukuda H, Matsuura N, Ono Y, Yao K, Hashimoto S, Terai S, Ohmori M, Tanaka K, Kato M, Shono T, Miyamoto H, Tanaka Y, Tada T. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation. Dig Endosc 2021; 33:1101-1109. [PMID: 33502046 DOI: 10.1111/den.13934] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/21/2021] [Accepted: 01/21/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVES Artificial intelligence (AI) systems have shown favorable performance in the detection of esophageal squamous cell carcinoma (ESCC). However, previous studies were limited by the quality of their validation methods. In this study, we evaluated the performance of an AI system with videos simulating situations in which ESCC has been overlooked. METHODS We used 17,336 images from 1376 superficial ESCCs and 1461 images from 196 noncancerous and normal esophagi to construct the AI system. To record validation videos, the endoscope was passed through the esophagus at a constant speed without focusing on the lesion to simulate situations in which ESCC has been missed. Validation videos were evaluated by the AI system and 21 endoscopists. RESULTS We prepared 100 video datasets, including 50 superficial ESCCs, 22 noncancerous lesions, and 28 normal esophagi. The AI system had sensitivity of 85.7% (54 of 63 ESCCs) and specificity of 40%. Initial evaluation by endoscopists conducted with plain video (without AI support) had average sensitivity of 75.0% (47.3 of 63 ESCC) and specificity of 91.4%. Subsequent evaluation by endoscopists was conducted with AI assistance, which improved their sensitivity to 77.7% (P = 0.00696) without changing their specificity (91.6%, P = 0.756). CONCLUSIONS Our AI system had high sensitivity for the detection of ESCC. As a support tool, the system has the potential to enhance detection of ESCC without reducing specificity. (UMIN000039645).
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Affiliation(s)
- Kotaro Waki
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | | | - Ayaka Shoji
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Takahiro Inoue
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Katsunori Matsueda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Muneaki Miyake
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yusaku Shimamoto
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Noriko Matsuura
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Yoichiro Ono
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Fukuoka, Japan
| | - Kenshi Yao
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Fukuoka, Japan
| | - Satoru Hashimoto
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Shuji Terai
- Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan
| | - Masayasu Ohmori
- Department of Gastroenterology, Okayama University Hospital, Okayama, Japan
| | - Kyosuke Tanaka
- Department of Endoscopic Medicine, Mie University Hospital, Mie, Japan
| | - Motohiko Kato
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Shono
- Department of Gastroenterology and Hepatology, Kumamoto Chuo Hospital, Kumamoto, Japan
| | - Hideaki Miyamoto
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Yasuhito Tanaka
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Tomohiro Tada
- AI Medical Service Inc, Tokyo, Japan
- Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
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Li N, Jin SZ. Artificial intelligence and early esophageal cancer. Artif Intell Gastrointest Endosc 2021; 2:198-210. [DOI: 10.37126/aige.v2.i5.198] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/23/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
The development of esophageal cancer (EC) from early to advanced stage results in a high mortality rate and poor prognosis. Advanced EC not only poses a serious threat to the life and health of patients but also places a heavy economic burden on their families and society. Endoscopy is of great value for the diagnosis of EC, especially in the screening of Barrett’s esophagus and early EC. However, at present, endoscopy has a low diagnostic rate for early tumors. In recent years, artificial intelligence (AI) has made remarkable progress in the diagnosis of digestive system tumors, providing a new model for clinicians to diagnose and treat these tumors. In this review, we aim to provide a comprehensive overview of how AI can help doctors diagnose early EC and precancerous lesions and make clinical decisions based on the predicted results. We analyze and summarize the recent research on AI and early EC. We find that based on deep learning (DL) and convolutional neural network methods, the current computer-aided diagnosis system has gradually developed from in vitro image analysis to real-time detection and diagnosis. Based on powerful computing and DL capabilities, the diagnostic accuracy of AI is close to or better than that of endoscopy specialists. We also analyze the shortcomings in the current AI research and corresponding improvement strategies. We believe that the application of AI-assisted endoscopy in the diagnosis of early EC and precancerous lesions will become possible after the further advancement of AI-related research.
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Affiliation(s)
- Ning Li
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
| | - Shi-Zhu Jin
- Department of Gastroenterology and Hepatology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang Province, China
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Kobara H, Uchita K, Uedo N, Kunikata J, Yorita K, Tada N, Nishiyama N, Shigehisa Y, Kuroiwa C, Matsuura N, Takahashi Y, Kai Y, Hanaoka U, Kiyohara Y, Kamiura S, Kanenishi K, Masaki T, Hirano K. Flexible Magnifying Endoscopy with Narrow Band Imaging for Diagnosing Uterine Cervical Neoplasms: A Multicenter Prospective Study. J Clin Med 2021; 10:jcm10204753. [PMID: 34682876 PMCID: PMC8536977 DOI: 10.3390/jcm10204753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/21/2022] Open
Abstract
We aimed to investigate the diagnostic ability of magnifying endoscopy with narrow band imaging (ME-NBI) for cervical intraepithelial neoplasia grade 2 or worse (CIN2+). This was a multicenter prospective study. Eligible patients had positive Pap smear results or follow-up high-grade cytology or CIN3 diagnosed in referring hospitals. Patients underwent ME-NBI by a gastrointestinal endoscopist, followed by colposcopy by a gynecologist. One lesion with the worst finding was considered the main lesion. Punch biopsies were collected from all indicated areas and one normal area. The reference standard was the highest histological grade among all biopsy specimens. The primary endpoint was the detection rate of patients with CIN2+ in the main lesion. The secondary endpoints were diagnostic ability for CIN2+ lesions and patients’ acceptability. We enrolled 88 patients. The detection rate of ME-NBI for patients with CIN2+ was 79% (95% CI: 66–88%; p = 1.000), which was comparable to that of colposcopy (79%; p = 1.000). For diagnosing CIN2+ lesions, ME-NBI showed a better sensitivity than colposcopy (87% vs. 74%, respectively; p = 0.302) but a lower specificity (50% vs. 68%, respectively; p = 0.210). Patients graded ME-NBI as having significantly less discomfort and involving less embarrassment than colposcopy. ME-NBI did not show a higher detection ability than colposcopy for patients with CIN2+, whereas it did show a better patient acceptability.
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Affiliation(s)
- Hideki Kobara
- Department of Gastroenterology and Neurology, Faculty of Medicine, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (N.T.); (N.N.); (T.M.)
- Correspondence: ; Tel.: +81-87-891-2156; Fax: +81-87-891-2158
| | - Kunihisa Uchita
- Department of Gastroenterology, Kochi Red Cross Hospital, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (K.U.); (Y.S.); (C.K.)
| | - Noriya Uedo
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; (N.U.); (N.M.)
| | - Jun Kunikata
- Department of Clinical Research Support Center, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan;
| | - Kenji Yorita
- Department of Diagnostic Pathology, Kochi Red Cross Hospital, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan;
| | - Naoya Tada
- Department of Gastroenterology and Neurology, Faculty of Medicine, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (N.T.); (N.N.); (T.M.)
| | - Noriko Nishiyama
- Department of Gastroenterology and Neurology, Faculty of Medicine, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (N.T.); (N.N.); (T.M.)
| | - Yuriko Shigehisa
- Department of Gastroenterology, Kochi Red Cross Hospital, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (K.U.); (Y.S.); (C.K.)
| | - Chihiro Kuroiwa
- Department of Gastroenterology, Kochi Red Cross Hospital, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (K.U.); (Y.S.); (C.K.)
| | - Noriko Matsuura
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan; (N.U.); (N.M.)
| | - Yohei Takahashi
- Department of Gynecology, Kochi Red Cross Hospital, Kochi, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (Y.T.); (Y.K.); (K.H.)
| | - Yuka Kai
- Department of Gynecology, Kochi Red Cross Hospital, Kochi, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (Y.T.); (Y.K.); (K.H.)
| | - Uiko Hanaoka
- Department of Perinatology and Gynecology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (U.H.); (K.K.)
| | - Yumiko Kiyohara
- Department of Gynecology, Osaka International Cancer Institute, 3-1-69, Otemae, Chuo-ku, Osaka 541-8567, Japan; (Y.K.); (S.K.)
- Department of Obstetrics and Gynecology, Japan Community Health Care Organization Osaka Hospital, 4-2-78 Fukushima, Fukushima-ku, Osaka 553-0003, Japan
| | - Shoji Kamiura
- Department of Gynecology, Osaka International Cancer Institute, 3-1-69, Otemae, Chuo-ku, Osaka 541-8567, Japan; (Y.K.); (S.K.)
| | - Kenji Kanenishi
- Department of Perinatology and Gynecology, Faculty of Medicine, Kagawa University, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (U.H.); (K.K.)
| | - Tsutomu Masaki
- Department of Gastroenterology and Neurology, Faculty of Medicine, 1750-1 Ikenobe, Miki, Kita, Kagawa 761-0793, Japan; (N.T.); (N.N.); (T.M.)
| | - Koki Hirano
- Department of Gynecology, Kochi Red Cross Hospital, Kochi, 2-13-51 Shinhonmachi, Kochi 780-8562, Japan; (Y.T.); (Y.K.); (K.H.)
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Xu J, Wang J, Bian X, Zhu JQ, Tie CW, Liu X, Zhou Z, Ni XG, Qian D. Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy. Laryngoscope 2021; 132:999-1007. [PMID: 34622964 DOI: 10.1002/lary.29894] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/18/2021] [Accepted: 09/28/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVES/HYPOTHESIS To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images. STUDY DESIGN Retrospective study. METHODS A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above-mentioned two modal images for prediction, we compared the proposed S-DCNN with two baseline models, namely DCNN-1 (only considering WLI images) and DCNN-2 (only considering NBI images). RESULTS In the threefold cross-validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%-96.5%) and 0.986 (95% CI 0.982-0.992), 87.0% (95% CI 84.2%-89.7%) and 0.930 (95% CI 0.906-0.961), and 92.8% (95% CI 90.4%-95.3%) and 0.971 (95% CI 0.953-0.992), respectively. The accuracy of S-DCNN is significantly improved compared with DCNN-1 (P-value <.001) and DCNN-2 (P-value = .008). CONCLUSION Using the deep-learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performance can be obtained by simultaneously utilizing the multimodal features of NBI image and WLI image of the same patient. LEVEL OF EVIDENCE 3 Laryngoscope, 2021.
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Affiliation(s)
- Jianwei Xu
- Deepwise Joint Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Wang
- Deepwise Joint Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xianzhang Bian
- Deepwise Joint Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoqing Liu
- Deepwise Artificial Intelligence Laboratory, Deepwise Healthcare, Beijing, China
| | - Zhiyong Zhou
- Deepwise Joint Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.,School of Design and Art, Shanghai Dianji University, Shanghai, China
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dahong Qian
- Deepwise Joint Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Tsai CL, Mukundan A, Chung CS, Chen YH, Wang YK, Chen TH, Tseng YS, Huang CW, Wu IC, Wang HC. Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer. Cancers (Basel) 2021; 13:4593. [PMID: 34572819 PMCID: PMC8469506 DOI: 10.3390/cancers13184593] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 02/08/2023] Open
Abstract
This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved.
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Affiliation(s)
- Cho-Lun Tsai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Arvind Mukundan
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
| | - Chen-Shuan Chung
- Department of Internal Medicine, Far Eastern Memorial Hospital, No.21, Sec. 2, Nanya S. Rd., Banciao Dist., New Taipei City 22060, Taiwan;
| | - Yi-Hsun Chen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Yao-Kuang Wang
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan; (Y.-K.W.); (I.-C.W.)
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
| | - Tsung-Hsien Chen
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chia Yi City 60002, Taiwan; (C.-L.T.); (Y.-H.C.); (T.-H.C.)
| | - Yu-Sheng Tseng
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
| | - Chien-Wei Huang
- Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan
- Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung City 90741, Taiwan
| | - I-Chen Wu
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan; (Y.-K.W.); (I.-C.W.)
- Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, No.100, Tzyou 1st Rd., Sanmin Dist., Kaohsiung City 80756, Taiwan
| | - Hsiang-Chen Wang
- Department of Mechanical Engineering, Advanced Institute of Manufacturing with High tech Innovations (AIM-HI) and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi County 62102, Taiwan; (A.M.); (Y.-S.T.)
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