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Wang M, Xu C, Fan K. An efficient fine tuning strategy of segment anything model for polyp segmentation. Sci Rep 2025; 15:14088. [PMID: 40269089 PMCID: PMC12019216 DOI: 10.1038/s41598-025-97802-w] [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: 01/28/2025] [Accepted: 04/07/2025] [Indexed: 04/25/2025] Open
Abstract
Colon cancer is a prevalent disease on a global scale, thus making its detection and prevention a critical area in the medical field. In addressing the challenges of high annotation costs and the need for improved accuracy in colon polyp detection, this study explores the segment anything model (SAM) application and fine-tuning strategies for colon polyp segmentation. Conventional full fine-tuning approaches frequently result in catastrophic forgetting, thereby compromising the model's generalization capabilities. To address this challenge, this paper proposes an efficient fine-tuning method, PSF-SAM, which mitigates catastrophic forgetting while enhancing performance in few-shot scenarios. This is achieved by freezing most SAM parameters and optimizing only specific structures. The efficacy of PSF-SAM is substantiated by experimental evaluations on the Kvasir-SEG and CVC-ClinicDB datasets, which demonstrate its superior performance in metrics such as mDice coefficients and mIoU, as well as its notable advantages in few-shot learning scenarios when compared to existing fine-tuning methods.
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Affiliation(s)
- Mingyan Wang
- Information Technology Center, Tsinghua University, Beijing, 100084, China.
| | - Cun Xu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China
| | - Kefeng Fan
- China Electronics Standardization Institute, Beijing, 100007, China
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Zhu Y, Yang RJ, Fu PY, Zhang Z, Zhang YZ, Li QL, Wang S, Zhou PH. Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition. Sci Data 2025; 12:212. [PMID: 39910061 PMCID: PMC11799166 DOI: 10.1038/s41597-025-04535-6] [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: 01/24/2025] [Indexed: 02/07/2025] Open
Abstract
Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collaboration in real-world settings. Eye-tracking data collection is considered a promising approach to uncovering how endoscopists and AI interact and influence each other during colonoscopy procedures. A common limitation of existing studies is their reliance on retrospective video clips, which fail to capture the dynamic demands of real-time colonoscopy, where endoscopists must simultaneously navigate the colonoscope and identify lesions on the screen. To address this gap, we established a dataset to analyze changes in endoscopists' eye movements during the colonoscopy withdrawal phase. Eye-tracking data was collected from graduate students, nurses, senior endoscopists, and novice endoscopists while they reviewed retrospectively recorded colonoscopy withdrawal videos, both with and without computer-aided detection (CADe) assistance. Furthermore, 80 real-time video segments were prospectively collected during endoscopists' actual colonoscopy withdrawal procedures, comprising 43 segments with CADe assistance and 37 segments without assistance (normal control).
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Affiliation(s)
- Yan Zhu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China
| | - Rui-Jie Yang
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
- School of Software Technology, Zhejiang University, Ningbo, 315100, China
| | - Pei-Yao Fu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China
| | - Zhen Zhang
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China
| | - Yi-Zhe Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Quan-Lin Li
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China.
| | - Shuo Wang
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China.
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, 200032, China.
- Data Science Institute, Imperial College London, London, SW7 2AZ, UK.
| | - Ping-Hong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, 200032, China.
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Ryu S, Imaizumi Y, Goto K, Iwauchi S, Kobayashi T, Ito R, Nakabayashi Y. Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery. Surg Endosc 2025; 39:1388-1396. [PMID: 39762611 PMCID: PMC11794642 DOI: 10.1007/s00464-024-11489-0] [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: 06/14/2024] [Accepted: 12/14/2024] [Indexed: 02/06/2025]
Abstract
BACKGROUND Devices that help educate young doctors and enable safe, minimally invasive surgery are needed. Eureka is a surgical artificial intelligence (AI) system that can intraoperatively highlight loose connective tissues (LCTs) in the dissected layers and nerves in the surgical field displayed on a monitor. In this study, we examined whether AI navigation (AIN) with Eureka can assist trainees in recognizing nerves during colorectal surgery. METHODS In left-sided colorectal surgery (n = 51, between July 2023 and February 2024), Eureka was connected to the laparoscopic system side by side, and the nerve was highlighted on the monitor during the surgery. We examined the rate of failure to recognize nerves by trainee surgeons over a total of 101 scenarios after it was recognized intraoperatively by the supervising surgeon (certified by the Japanese Society of Endoscopic Surgery). We also examined the frequency of nerve recognition by the trainee physicians viewing the Eureka monitor when recognition was not possible (recognition assistance rate). RESULTS The nerve recognition failure rate and recognition assistance rate with AIN were as follows: right hypogastric nerve during sigmoid colon mobilization, 44/101 (43.6%) and 19/44 (43.2%); left hypogastric nerves during dissection of the dorsal rectum, 27/101 (26.7%) and 13/27 (48.1%); right lumbar splanchnic nerves, 32/101 (31.7%) and 29/32 (90.6%); left lumbar splanchnic nerves, 44/101 (43.6%) and 39/44 (88.6%); and pelvic visceral nerves during dissection of the dorsal rectum, 29/45 (64.4%) and 6/29 (20.7%), respectively. CONCLUSION Although the rate of recognition with assistance from AIN differed for the different nerves, this system can potentially assist in anatomic recognition, enhance surgical education, and contribute to nerve preservation. TRIAL REGISTRATION Improvement of AI navigation in minimally invasive surgery and examination of its intraoperative support and educational effectiveness. Research Ethics Committee of the Kawaguchi Municipal Medical Center (Saitama, Japan) approval number: 2022-27.
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Affiliation(s)
- Shunjin Ryu
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan.
| | - Yuta Imaizumi
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
| | - Keisuke Goto
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
| | - Sotaro Iwauchi
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
| | - Takehiro Kobayashi
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
| | - Ryusuke Ito
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
| | - Yukio Nakabayashi
- Department of Digestive Surgery, Kawaguchi Municipal Medical Center, Kawaguchi City, Saitama, 180, Nishiaraijuku333-0833, Japan
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Jain A, Sinha S, Mazumdar S. Comparative analysis of machine learning frameworks for automatic polyp characterization. Biomed Signal Process Control 2024; 95:106451. [DOI: 10.1016/j.bspc.2024.106451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
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He H, Zhu J, Ye Z, Bao H, Shou J, Liu Y, Chen F. Using multimodal ultrasound including full-time-series contrast-enhanced ultrasound cines for identifying the nature of thyroid nodules. Front Oncol 2024; 14:1340847. [PMID: 39267842 PMCID: PMC11390443 DOI: 10.3389/fonc.2024.1340847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 08/07/2024] [Indexed: 09/15/2024] Open
Abstract
Background Based on the conventional ultrasound images of thyroid nodules, contrast-enhanced ultrasound (CEUS) videos were analyzed to investigate whether CEUS improves the classification accuracy of benign and malignant thyroid nodules using machine learning (ML) radiomics and compared with radiologists. Materials and methods The B-mode ultrasound (B-US), real-time elastography (RTE), color doppler flow images (CDFI) and CEUS cines of patients from two centers were retrospectively gathered. Then, the region of interest (ROI) was delineated to extract the radiomics features. Seven ML algorithms combined with four kinds of radiomics data (B-US, B-US + CDFI + RTE, CEUS, and B-US + CDFI + RTE + CEUS) were applied to establish 28 models. The diagnostic performance of ML models was compared with interpretations from expert and nonexpert readers. Results A total of 181 thyroid nodules from 181 patients of 64 men (mean age, 42 years +/- 12) and 117 women (mean age, 46 years +/- 12) were included. Adaptive boosting (AdaBoost) achieved the highest area under the receiver operating characteristic curve (AUC) of 0.89 in the test set among 28 models when combined with B-US + CDFI + RTE + CEUS data and an AUC of 0.72 and 0.66 when combined with B-US and B-US + CDFI + RTE data. The AUC achieved by senior and junior radiologists was 0.78 versus (vs.) 0.69 (p > 0.05), 0.79 vs. 0.64 (p < 0.05), and 0.88 vs. 0.69 (p < 0.05) combined with B-US, B-US+CDFI+RTE and B-US+CDFI+RTE+CEUS, respectively. Conclusion With the addition of CEUS, the diagnostic performance was enhanced for all seven classifiers and senior radiologists based on conventional ultrasound images, while no enhancement was observed for junior radiologists. The diagnostic performance of ML models was similar to senior radiologists, but superior to those junior radiologists.
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Affiliation(s)
- Hanlu He
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Junyan Zhu
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhengdu Ye
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Haiwei Bao
- Department of Ultrasound, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jinduo Shou
- Department of Ultrasound, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ying Liu
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - Fen Chen
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Ultrasound, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Huang X, Wang L, Jiang S, Xu L. DHAFormer: Dual-channel hybrid attention network with transformer for polyp segmentation. PLoS One 2024; 19:e0306596. [PMID: 38985710 PMCID: PMC11236112 DOI: 10.1371/journal.pone.0306596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 06/17/2024] [Indexed: 07/12/2024] Open
Abstract
The accurate early diagnosis of colorectal cancer significantly relies on the precise segmentation of polyps in medical images. Current convolution-based and transformer-based segmentation methods show promise but still struggle with the varied sizes and shapes of polyps and the often low contrast between polyps and their background. This research introduces an innovative approach to confronting the aforementioned challenges by proposing a Dual-Channel Hybrid Attention Network with Transformer (DHAFormer). Our proposed framework features a multi-scale channel fusion module, which excels at recognizing polyps across a spectrum of sizes and shapes. Additionally, the framework's dual-channel hybrid attention mechanism is innovatively conceived to reduce background interference and improve the foreground representation of polyp features by integrating local and global information. The DHAFormer demonstrates significant improvements in the task of polyp segmentation compared to currently established methodologies.
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Affiliation(s)
- Xuejie Huang
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Liejun Wang
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Shaochen Jiang
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
| | - Lianghui Xu
- School of Computer Science and Technology, Xinjiang University, Urumqi, China
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Wan JJ, Zhu PC, Chen BL, Yu YT. A semantic feature enhanced YOLOv5-based network for polyp detection from colonoscopy images. Sci Rep 2024; 14:15478. [PMID: 38969765 PMCID: PMC11226707 DOI: 10.1038/s41598-024-66642-5] [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: 09/26/2023] [Accepted: 07/03/2024] [Indexed: 07/07/2024] Open
Abstract
Colorectal cancer (CRC) is a common digestive system tumor with high morbidity and mortality worldwide. At present, the use of computer-assisted colonoscopy technology to detect polyps is relatively mature, but it still faces some challenges, such as missed or false detection of polyps. Therefore, how to improve the detection rate of polyps more accurately is the key to colonoscopy. To solve this problem, this paper proposes an improved YOLOv5-based cancer polyp detection method for colorectal cancer. The method is designed with a new structure called P-C3 incorporated into the backbone and neck network of the model to enhance the expression of features. In addition, a contextual feature augmentation module was introduced to the bottom of the backbone network to increase the receptive field for multi-scale feature information and to focus on polyp features by coordinate attention mechanism. The experimental results show that compared with some traditional target detection algorithms, the model proposed in this paper has significant advantages for the detection accuracy of polyp, especially in the recall rate, which largely solves the problem of missed detection of polyps. This study will contribute to improve the polyp/adenoma detection rate of endoscopists in the process of colonoscopy, and also has important significance for the development of clinical work.
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Affiliation(s)
- Jing-Jing Wan
- Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian, 223023, Jiangsu, China.
| | - Peng-Cheng Zhu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
| | - Bo-Lun Chen
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
| | - Yong-Tao Yu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
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Saito Y, Sakamoto T, Dekker E, Pioche M, Probst A, Ponchon T, Messmann H, Dinis-Ribeiro M, Matsuda T, Ikematsu H, Saito S, Wada Y, Oka S, Sano Y, Fujishiro M, Murakami Y, Ishikawa H, Inoue H, Tanaka S, Tajiri H. First report from the International Evaluation of Endoscopic classification Japan NBI Expert Team: International multicenter web trial. Dig Endosc 2024; 36:591-599. [PMID: 37702082 DOI: 10.1111/den.14682] [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: 06/29/2023] [Accepted: 09/10/2023] [Indexed: 09/14/2023]
Abstract
OBJECTIVES Narrow-band imaging (NBI) contributes to real-time optical diagnosis and classification of colorectal lesions. The Japan NBI Expert Team (JNET) was introduced in 2011. The aim of this study was to explore the diagnostic accuracy of JNET when applied by European and Japanese endoscopists not familiar with this classification. METHODS This study was conducted by 36 European Society of Gastrointestinal Endoscopy (ESGE) and 49 Japan Gastroenterological Endoscopy Society (JGES) non-JNET endoscopists using still images of 150 lesions. For each lesion, nonmagnified white-light, nonmagnified NBI, and magnified NBI images were presented. In the magnified NBI, the evaluation area was designated by region of interest (ROI). The endoscopists scored histological prediction for each lesion. RESULTS In ESGE members, the sensitivity, specificity, and accuracy were respectively 73.3%, 94.7%, and 93.0% for JNET Type 1; 53.0%, 64.9%, and 62.1% for Type 2A; 43.9%, 67.7%, and 55.1% for Type 2B; and 38.1%, 93.7%, and 85.1% for Type 3. When Type 2B and 3 were considered as one category of cancer, the sensitivity, specificity, and accuracy for differentiating high-grade dysplasia and cancer from the others were 59.9%, 72.5%, and 63.8%, respectively. These trends were the same for JGES endoscopists. CONCLUSION The diagnostic accuracy of the JNET classification was similar between ESGE and JGES and considered to be sufficient for JNET Type 1. On the other hand, the accuracy for Types 2 and 3 is not sufficient; however, JNET 2B lesions should be resected en bloc due to the risk of cancers and JNET 3 can be treated by surgery due to its high specificity.
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Affiliation(s)
- Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Taku Sakamoto
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- University of Tsukuba, Ibaraki, Japan
| | - Evelien Dekker
- Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | - Andreas Probst
- RISE@CI-IPO, Portuguese Oncology Institute of Porto/Porto Comprehensive Cancer Center, Porto, Portugal
| | | | - Helmut Messmann
- RISE@CI-IPO, Portuguese Oncology Institute of Porto/Porto Comprehensive Cancer Center, Porto, Portugal
| | | | - Takahisa Matsuda
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
- Toho University, Tokyo, Japan
| | | | | | | | - Shiro Oka
- Hiroshima University, Hiroshima, Japan
| | | | | | | | | | | | - Shinji Tanaka
- Hiroshima University, Hiroshima, Japan
- JA Onomichi General Hospital, Hiroshima, Japan
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Sikkandar MY, Sundaram SG, Alassaf A, AlMohimeed I, Alhussaini K, Aleid A, Alolayan SA, Ramkumar P, Almutairi MK, Begum SS. Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer. Sci Rep 2024; 14:7318. [PMID: 38538774 PMCID: PMC11377543 DOI: 10.1038/s41598-024-57993-0] [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: 09/11/2023] [Accepted: 03/24/2024] [Indexed: 09/07/2024] Open
Abstract
Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.
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Affiliation(s)
- Mohamed Yacin Sikkandar
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
| | - Sankar Ganesh Sundaram
- Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
| | - Ahmad Alassaf
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - Ibrahim AlMohimeed
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - Khalid Alhussaini
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
| | - Adham Aleid
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
| | - Salem Ali Alolayan
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - P Ramkumar
- Department of Computer Science and Engineering, Sri Sairam College of Engineering, Anekal, Bengaluru, 562106, Karnataka, India
| | - Meshal Khalaf Almutairi
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - S Sabarunisha Begum
- Department of Biotechnology, P.S.R. Engineering College, Sivakasi, 626140, India
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Pewton SW, Cassidy B, Kendrick C, Yap MH. Dermoscopic dark corner artifacts removal: Friend or foe? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107986. [PMID: 38157827 DOI: 10.1016/j.cmpb.2023.107986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/09/2023] [Accepted: 12/16/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND AND OBJECTIVES One of the more significant obstacles in classification of skin cancer is the presence of artifacts. This paper investigates the effect of dark corner artifacts, which result from the use of dermoscopes, on the performance of a deep learning binary classification task. Previous research attempted to remove and inpaint dark corner artifacts, with the intention of creating an ideal condition for models. However, such research has been shown to be inconclusive due to a lack of available datasets with corresponding labels for dark corner artifact cases. METHODS To address these issues, we label 10,250 skin lesion images from publicly available datasets and introduce a balanced dataset with an equal number of melanoma and non-melanoma cases. The training set comprises 6126 images without artifacts, and the testing set comprises 4124 images with dark corner artifacts. We conduct three experiments to provide new understanding on the effects of dark corner artifacts, including inpainted and synthetically generated examples, on a deep learning method. RESULTS Our results suggest that introducing synthetic dark corner artifacts which have been superimposed onto the training set improved model performance, particularly in terms of the true negative rate. This indicates that deep learning learnt to ignore dark corner artifacts, rather than treating it as melanoma, when dark corner artifacts were introduced into the training set. Further, we propose a new approach to quantifying heatmaps indicating network focus using a root mean square measure of the brightness intensity in the different regions of the heatmaps. CONCLUSIONS The proposed artifact methods can be used in future experiments to help alleviate possible impacts on model performance. Additionally, the newly proposed heatmap quantification analysis will help to better understand the relationships between heatmap results and other model performance metrics.
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Affiliation(s)
- Samuel William Pewton
- Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.
| | - Bill Cassidy
- Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.
| | - Connah Kendrick
- Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.
| | - Moi Hoon Yap
- Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Chester Street, Manchester, M1 5GD, UK.
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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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Goetz N, Hanigan K, Cheng RKY. Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study. Artif Intell Gastrointest Endosc 2023; 4:18-26. [DOI: 10.37126/aige.v4.i2.18] [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: 09/04/2023] [Revised: 11/07/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy (AIAC) outside of clinical trials.
AIM To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy (CC).
METHODS This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane, Australia. CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023. Endoscopists who conducted over 50 procedures before and after AIAC introduction were included. Procedures for surveillance of inflammatory bowel disease were excluded. Patient demographics, proceduralist specialisation, indication for colonoscopy, and colonoscopy quality metrics were collected. Adenoma detection rate (ADR) and sessile serrated lesion detection rate (SSLDR) were calculated for both AIAC and CC.
RESULTS The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists. Baseline patient demographics were similar, with median age of 60 years with a slight female predominance (52.1%). Procedure indications, bowel preparation quality, and caecal intubation rates were comparable between groups. AIAC had a slightly longer withdrawal time compared to CC, but the difference was not statistically significant. The introduction of AIAC did not significantly change ADR (52.1% for AIAC vs 52.6% for CC, P = 0.91) or SSLDR (17.4% for AIAC vs 18.1% for CC, P = 0.44).
CONCLUSION The implementation of AIAC failed to improve key markers of colonoscopy quality, including ADR, SSLDR and withdrawal time. Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists.
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Affiliation(s)
- Naeman Goetz
- Department of Gastroenterology, Redcliffe Hospital, Redcliffe 4020, Australia
| | - Katherine Hanigan
- Department of Gastroenterology, Redcliffe Hospital, Redcliffe 4020, Australia
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Brunori A, Daca-Alvarez M, Pellisé M. pT1 colorectal cancer: A treatment dilemma. Best Pract Res Clin Gastroenterol 2023; 66:101854. [PMID: 37852711 DOI: 10.1016/j.bpg.2023.101854] [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: 05/17/2023] [Revised: 07/04/2023] [Accepted: 07/30/2023] [Indexed: 10/20/2023]
Abstract
The implementation of population screening programs for colorectal cancer (CRC) has led to a considerable increase in the prevalence pT1-CRC originating on polyps amenable by local treatments. However, a high proportion of patients are referred for unnecessary oncological surgeries without a clear benefit in terms of survival. Selecting the appropriate endoscopic resection technique in the moment of diagnosis becomes crucial to provide the best treatment alternative to each individual polyp and patient. For this, it is imperative to increase the optical diagnostic skill for differentiating pT1-CRCs and decide the appropriate initial therapy. En bloc resection is crucial to obtain an adequate histological specimen that might allow organ preserving therapeutic management. In this review, we address key challenges in T1 CRC management, explore the efficacy and safety of the available diagnostic and therapeutic approaches, and shed light on upcoming advances in the field.
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Affiliation(s)
- Angelo Brunori
- Gastroenterology and Digestive Endoscopy, Università degli Studi di Perugia, Italy
| | - Maria Daca-Alvarez
- Department of Gastroenterology Hospital Clinic de Barcelona, Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Centro de Investigación Biomédica en Red de EnfermedadesHepáticas y Digestivas (CIBERehd), Spain
| | - Maria Pellisé
- Department of Gastroenterology Hospital Clinic de Barcelona, Institut d'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Centro de InvestigaciónBiomé, dica en Red de EnfermedadesHepáticas y Digestivas (CIBERehd), Universitat de Barcelona, Barcelona, Spain.
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Nemoto D, Guo Z, Katsuki S, Takezawa T, Maemoto R, Kawasaki K, Inoue K, Akutagawa T, Tanaka H, Sato K, Omori T, Takanashi K, Hayashi Y, Nakajima Y, Miyakura Y, Matsumoto T, Yoshida N, Esaki M, Uraoka T, Kato H, Inoue Y, Peng B, Zhang R, Hisabe T, Matsuda T, Yamamoto H, Tanaka N, Lefor AK, Zhu X, Togashi K. Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos). Gastrointest Endosc 2023; 98:90-99.e4. [PMID: 36738793 DOI: 10.1016/j.gie.2023.01.050] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/05/2023] [Accepted: 01/25/2023] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS Differentiation of colorectal cancers (CRCs) with deep submucosal invasion (T1b) from CRCs with superficial invasion (T1a) or no invasion (Tis) is not straightforward. This study aimed to develop a computer-aided diagnosis (CADx) system to establish the diagnosis of early-stage cancers using nonmagnified endoscopic white-light images alone. METHODS From 5108 images, 1513 lesions (Tis, 1074; T1a, 145; T1b, 294) were collected from 1470 patients at 10 academic hospitals and assigned to training and testing datasets (3:1). The ResNet-50 network was used as the backbone to extract features from images. Oversampling and focal loss were used to compensate class imbalance of the invasive stage. Diagnostic performance was assessed using the testing dataset including 403 CRCs with 1392 images. Two experts and 2 trainees read the identical testing dataset. RESULTS At a 90% cutoff for the per-lesion score, CADx showed the highest specificity of 94.4% (95% confidence interval [CI], 91.3-96.6), with 59.8% (95% CI, 48.3-70.4) sensitivity and 87.3% (95% CI, 83.7-90.4) accuracy. The area under the characteristic curve was 85.1% (95% CI, 79.9-90.4) for CADx, 88.2% (95% CI, 83.7-92.8) for expert 1, 85.9% (95% CI, 80.9-90.9) for expert 2, 77.0% (95% CI, 71.5-82.4) for trainee 1 (vs CADx; P = .0076), and 66.2% (95% CI, 60.6-71.9) for trainee 2 (P < .0001). The function was also confirmed on 9 short videos. CONCLUSIONS A CADx system developed with endoscopic white-light images showed excellent per-lesion specificity and accuracy for T1b lesion diagnosis, equivalent to experts and superior to trainees. (Clinical trial registration number: UMIN000037053.).
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Affiliation(s)
- Daiki Nemoto
- Department of Coloproctology, Aizu Medical Center Fukushima Medical University, Aizuwakamatsu, Japan
| | - Zhe Guo
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Japan; Department of Laboratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Shinichi Katsuki
- Department of Gastroenterology, Otaru Ekisaikai Hospital, Otaru, Japan
| | - Takahito Takezawa
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, Shimotsuke, Japan
| | - Ryo Maemoto
- Department of Surgery, Saitama Medical Center, Jichi Medical University, Saitama, Japan
| | - Keisuke Kawasaki
- Department of Gastroenterology, Iwate Medical University, Morioka, Japan
| | - Ken Inoue
- Department of Molecular Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Takashi Akutagawa
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Hirohito Tanaka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Koichiro Sato
- Department of Clinical Laboratory and Endoscopy, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Teppei Omori
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | | | - Yoshikazu Hayashi
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, Shimotsuke, Japan
| | - Yuki Nakajima
- Department of Coloproctology, Aizu Medical Center Fukushima Medical University, Aizuwakamatsu, Japan
| | - Yasuyuki Miyakura
- Department of Surgery, Saitama Medical Center, Jichi Medical University, Saitama, Japan
| | - Takayuki Matsumoto
- Department of Gastroenterology, Iwate Medical University, Morioka, Japan
| | - Naohisa Yoshida
- Department of Molecular Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Motohiro Esaki
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Hiroyuki Kato
- Department of Clinical Laboratory and Endoscopy, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Yuji Inoue
- Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Boyuan Peng
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Japan
| | - Ruiyao Zhang
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Japan
| | - Takashi Hisabe
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Fukuoka, Japan
| | - Tomoki Matsuda
- Department of Gastroenterology, Sendai Kosei Hospital, Sendai, Japan
| | - Hironori Yamamoto
- Department of Medicine, Division of Gastroenterology, Jichi Medical University, Shimotsuke, Japan
| | - Noriko Tanaka
- Health Data Science Research Section, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | | | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Aizuwakamatsu, Japan
| | - Kazutomo Togashi
- Department of Coloproctology, Aizu Medical Center Fukushima Medical University, Aizuwakamatsu, Japan
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Stanciu SG, König K, Song YM, Wolf L, Charitidis CA, Bianchini P, Goetz M. Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning. BIOPHYSICS REVIEWS 2023; 4:021307. [PMID: 38510341 PMCID: PMC10903409 DOI: 10.1063/5.0133027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/26/2023] [Indexed: 03/22/2024]
Abstract
According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.
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Affiliation(s)
- Stefan G. Stanciu
- Center for Microscopy-Microanalysis and Information Processing, University Politehnica of Bucharest, Bucharest, Romania
| | | | | | - Lior Wolf
- School of Computer Science, Tel Aviv University, Tel-Aviv, Israel
| | - Costas A. Charitidis
- Research Lab of Advanced, Composite, Nano-Materials and Nanotechnology, School of Chemical Engineering, National Technical University of Athens, Athens, Greece
| | - Paolo Bianchini
- Nanoscopy and NIC@IIT, Italian Institute of Technology, Genoa, Italy
| | - Martin Goetz
- Medizinische Klinik IV-Gastroenterologie/Onkologie, Kliniken Böblingen, Klinikverbund Südwest, Böblingen, Germany
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Hong SM, Baek DH. A Review of Colonoscopy in Intestinal Diseases. Diagnostics (Basel) 2023; 13:diagnostics13071262. [PMID: 37046479 PMCID: PMC10093393 DOI: 10.3390/diagnostics13071262] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/25/2023] [Accepted: 03/26/2023] [Indexed: 03/30/2023] Open
Abstract
Since the development of the fiberoptic colonoscope in the late 1960s, colonoscopy has been a useful tool to diagnose and treat various intestinal diseases. This article reviews the clinical use of colonoscopy for various intestinal diseases based on present and future perspectives. Intestinal diseases include infectious diseases, inflammatory bowel disease (IBD), neoplasms, functional bowel disorders, and others. In cases of infectious diseases, colonoscopy is helpful in making the differential diagnosis, revealing endoscopic gross findings, and obtaining the specimens for pathology. Additionally, colonoscopy provides clues for distinguishing between infectious disease and IBD, and aids in the post-treatment monitoring of IBD. Colonoscopy is essential for the diagnosis of neoplasms that are diagnosed through only pathological confirmation. At present, malignant tumors are commonly being treated using endoscopy because of the advancement of endoscopic resection procedures. Moreover, the characteristics of tumors can be described in more detail by image-enhanced endoscopy and magnifying endoscopy. Colonoscopy can be helpful for the endoscopic decompression of colonic volvulus in large bowel obstruction, balloon dilatation as a treatment for benign stricture, and colon stenting as a treatment for malignant obstruction. In the diagnosis of functional bowel disorder, colonoscopy is used to investigate other organic causes of the symptom.
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17
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Rondonotti E, Di Paolo D, Rizzotto ER, Alvisi C, Buscarini E, Spadaccini M, Tamanini G, Paggi S, Amato A, Scardino G, Romeo S, Alicante S, Ancona F, Guido E, Marzo V, Chicco F, Agazzi S, Rosa C, Correale L, Repici A, Hassan C, Radaelli F. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy 2022; 54:1171-1179. [PMID: 35545122 DOI: 10.1055/a-1849-6878] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Computer-aided detection (CADe) increases adenoma detection in primary screening colonoscopy. The potential benefit of CADe in a fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening program is unknown. This study assessed whether use of CADe increases the adenoma detection rate (ADR) in a FIT-based CRC screening program. METHODS In a multicenter, randomized trial, FIT-positive individuals aged 50-74 years undergoing colonoscopy, were randomized (1:1) to receive high definition white-light (HDWL) colonoscopy, with or without a real-time deep-learning CADe by endoscopists with baseline ADR > 25 %. The primary outcome was ADR. Secondary outcomes were mean number of adenomas per colonoscopy (APC) and advanced adenoma detection rate (advanced-ADR). Subgroup analysis according to baseline endoscopists' ADR (≤ 40 %, 41 %-45 %, ≥ 46 %) was also performed. RESULTS 800 individuals (median age 61.0 years [interquartile range 55-67]; 409 men) were included: 405 underwent CADe-assisted colonoscopy and 395 underwent HDWL colonoscopy alone. ADR and APC were significantly higher in the CADe group than in the HDWL arm: ADR 53.6 % (95 %CI 48.6 %-58.5 %) vs. 45.3 % (95 %CI 40.3 %-50.45 %; RR 1.18; 95 %CI 1.03-1.36); APC 1.13 (SD 1.54) vs. 0.90 (SD 1.32; P = 0.03). No significant difference in advanced-ADR was found (18.5 % [95 %CI 14.8 %-22.6 %] vs. 15.9 % [95 %CI 12.5 %-19.9 %], respectively). An increase in ADR was observed in all endoscopist groups regardless of baseline ADR. CONCLUSIONS Incorporating CADe significantly increased ADR and APC in the framework of a FIT-based CRC screening program. The impact of CADe appeared to be consistent regardless of endoscopist baseline ADR.
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Affiliation(s)
| | - Dhanai Di Paolo
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, Milan, Italy
| | - Erik Rosa Rizzotto
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | | | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | | | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | - Samanta Romeo
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Saverio Alicante
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Fabio Ancona
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | - Ennio Guido
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | - Fabio Chicco
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | | | - Cesare Rosa
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | - Loredana Correale
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
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18
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Gan PL, Huang S, Pan X, Xia HF, Lü MH, Zhou X, Tang XW. The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis. Medicine (Baltimore) 2022; 101:e31931. [PMID: 36451438 PMCID: PMC9704924 DOI: 10.1097/md.0000000000031931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022] Open
Abstract
Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the development trends and research hotspots of AI in digestive endoscopy by visualizing articles. Publications on AI in digestive endoscopy research were retrieved from the Web of Science Core Collection on April 25, 2022. VOSviewer and CiteSpace were used to assess and plot the research outputs. This analytical research was based on original articles and reviews. A total of 524 records of AI research in digestive endoscopy, published between 2005 and 2022, were retrieved. The number of articles has increased 27-fold from 2017 to 2021. Fifty-one countries and 994 institutions contributed to all publications. Asian countries had the highest number of publications. China, the USA, and Japan were consistently the leading driving forces and mainly contributed (26%, 21%, and 14.31%, respectively). With a solid academic reputation in this area, Japan has the highest number of citations per article. Tada Tomohiro published the most articles and received the most citations.. Gastrointestinal endoscopy published the largest number of publications, and 4 of the top 10 cited papers were published in this journal. "The Classification," "ulcerative colitis," "capsule endoscopy," "polyp detection," and "early gastric cancer" were the leading research hotspots. Our study provides systematic elaboration for researchers to better understand the development of AI in gastrointestinal endoscopy.
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Affiliation(s)
- Pei-Ling Gan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shu Huang
- Department of Gastroenterology, the People’s Hospital of Lianshui, Huaian, China
| | - Xiao Pan
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hui-Fang Xia
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Mu-Han Lü
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiao-Wei Tang
- Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University, Luzhou, China
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Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Cherubini A. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep 2022; 12:14952. [PMID: 36056152 PMCID: PMC9440124 DOI: 10.1038/s41598-022-18751-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/18/2022] [Indexed: 11/25/2022] Open
Abstract
Artificial Intelligence (AI) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between MDs and AI enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human-AI collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an AI support system. Endoscopists were influenced by AI ([Formula: see text]), but not erratically: they followed the AI advice more when it was correct ([Formula: see text]) than incorrect ([Formula: see text]). Endoscopists achieved this outcome through a weighted integration of their and the AI opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human-AI hybrid team to outperform both agents taken alone. We discuss the features of the human-AI interaction that determined this favorable outcome.
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Affiliation(s)
- Carlo Reverberi
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy.
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy.
| | - Tommaso Rigon
- Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126, Milan, Italy
| | - Aldo Solari
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy
- Department of Economics, Management and Statistics, University of Milano-Bicocca, 20126, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, 20072, Pieve Emanuele, Italy
- Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy
| | - Paolo Cherubini
- Department of Psychology, University of Milano-Bicocca, 20126, Milan, Italy
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy
- Department of Neural and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Andrea Cherubini
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126, Milan, Italy.
- Artificial Intelligence Group, Cosmo AI/Linkverse, Lainate, 20045, Milan, Italy.
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20
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Shaukat A, Lichtenstein DR, Somers SC, Chung DC, Perdue DG, Gopal M, Colucci DR, Phillips SA, Marka NA, Church TR, Brugge WR. Computer-Aided Detection Improves Adenomas per Colonoscopy for Screening and Surveillance Colonoscopy: A Randomized Trial. Gastroenterology 2022; 163:732-741. [PMID: 35643173 DOI: 10.1053/j.gastro.2022.05.028] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/05/2022] [Accepted: 05/13/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND & AIMS Colonoscopy for colorectal cancer screening is endoscopist dependent, and colonoscopy quality improvement programs aim to improve efficacy. This study evaluated the clinical benefit and safety of using a computer-aided detection (CADe) device in colonoscopy procedures. METHODS This randomized study prospectively evaluated the use of a CADe device at 5 academic and community centers by US board-certified gastroenterologists (n = 22). Participants aged ≥40 scheduled for screening or surveillance (≥3 years) colonoscopy were included; exclusion criteria included incomplete procedure, diagnostic indication, inflammatory bowel disease, and familial adenomatous polyposis. Patients were randomized by endoscopist to the standard or CADe colonoscopy arm using computer-generated, random-block method. The 2 primary endpoints were adenomas per colonoscopy (APC), the total number of adenomas resected divided by the total number of colonoscopies; and true histology rate (THR), the proportion of resections with clinically significant histology divided by the total number of polyp resections. The primary analysis used a modified intention-to-treat approach. RESULTS Between January and September 2021, 1440 participants were enrolled to be randomized. After exclusion of participants who did not meet the eligibility criteria, 677 in the standard arm and 682 in the CADe arm were included in a modified intention-to-treat analysis. APC increased significantly with use of the CADe device (standard vs CADe: 0.83 vs 1.05, P = .002; total number of adenomas, 562 vs 719). There was no decrease in THR with use of the CADe device (standard vs CADe: 71.7% vs 67.4%, P for noninferiority < .001; total number of non-neoplastic lesions, 284 vs 375). Adenoma detection rate was 43.9% and 47.8% in the standard and CADe arms, respectively (P = .065). CONCLUSIONS For experienced endoscopists performing screening and surveillance colonoscopies in the United States, the CADe device statistically improved overall adenoma detection (APC) without a concomitant increase in resection of non-neoplastic lesions (THR). CLINICALTRIALS gov registration: NCT04754347.
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Affiliation(s)
- Aasma Shaukat
- Division of Gastroenterology and Hepatology, Department of Medicine, New York University Grossman School of Medicine, New York, New York; Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, Minnesota.
| | - David R Lichtenstein
- Division of Gastroenterology, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
| | - Samuel C Somers
- Concord Hospital Gastroenterology/Concord Endoscopy Center, Concord, New Hampshire
| | - Daniel C Chung
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | | | | | - Nicholas A Marka
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, Minnesota
| | - Timothy R Church
- Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, Minnesota
| | - William R Brugge
- Division of Gastroenterology, Department of Medicine, Mount Auburn Hospital, Harvard Medical School, Boston, Massachusetts
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Shaukat A, Tuskey A, Rao VL, Dominitz JA, Murad MH, Keswani RN, Bazerbachi F, Day LW. Interventions to improve adenoma detection rates for colonoscopy. Gastrointest Endosc 2022; 96:171-183. [PMID: 35680469 DOI: 10.1016/j.gie.2022.03.026] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 03/25/2022] [Indexed: 02/08/2023]
Affiliation(s)
- Aasma Shaukat
- Division of Gastroenterology, Department of Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Anne Tuskey
- Division of Gastroenterology, Department of Medicine, University of Virginia, Arlington, Virginia, USA
| | - Vijaya L Rao
- Section of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Jason A Dominitz
- Division of Gastroenterology, Department of Medicine, Puget Sound Veterans Affairs Medical Center and University of Washington, Seattle, Washington, USA
| | - M Hassan Murad
- Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Rajesh N Keswani
- Division of Gastroenterology, Department of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Fateh Bazerbachi
- Division of Gastroenterology, CentraCare, Interventional Endoscopy Program, St Cloud, Minnesota, USA
| | - Lukejohn W Day
- Division of Gastroenterology, Department of Medicine, Zuckerberg San Francisco General Hospital and University of San Francisco, San Francisco, California, USA
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22
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Adjei PE, Lonseko ZM, Du W, Zhang H, Rao N. Examining the effect of synthetic data augmentation in polyp detection and segmentation. Int J Comput Assist Radiol Surg 2022; 17:1289-1302. [PMID: 35678960 DOI: 10.1007/s11548-022-02651-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/21/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE As with several medical image analysis tasks based on deep learning, gastrointestinal image analysis is plagued with data scarcity, privacy concerns and an insufficient number of pathology samples. This study examines the generation and utility of synthetic samples of colonoscopy images with polyps for data augmentation. METHODS We modify and train a pix2pix model to generate synthetic colonoscopy samples with polyps to augment the original dataset. Subsequently, we create a variety of datasets by varying the quantity of synthetic samples and traditional augmentation samples, to train a U-Net network and Faster R-CNN model for segmentation and detection of polyps, respectively. We compare the performance of the models when trained with the resulting datasets in terms of F1 score, intersection over union, precision and recall. Further, we compare the performances of the models with unseen polyp datasets to assess their generalization ability. RESULTS The average F1 coefficient and intersection over union are improved with increasing number of synthetic samples in U-Net over all test datasets. The performance of the Faster R-CNN model is also improved in terms of polyp detection, while decreasing the false-negative rate. Further, the experimental results for polyp detection outperform similar studies in the literature on the ETIS-PolypLaribDB dataset. CONCLUSION By varying the quantity of synthetic and traditional augmentation, there is the potential to control the sensitivity of deep learning models in polyp segmentation and detection. Further, GAN-based augmentation is a viable option for improving the performance of models for polyp segmentation and detection.
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Affiliation(s)
- Prince Ebenezer Adjei
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.,Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Zenebe Markos Lonseko
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Wenju Du
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Han Zhang
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 610054, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Nini Rao
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, 610054, China. .,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
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23
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Zacharakis G, Almasoud A. Using of artificial intelligence: Current and future applications in colorectal cancer screening. World J Gastroenterol 2022; 28:2778-2781. [PMID: 35979167 PMCID: PMC9260867 DOI: 10.3748/wjg.v28.i24.2778] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/26/2022] [Accepted: 06/13/2022] [Indexed: 02/06/2023] Open
Abstract
Significant developments in colorectal cancer screening are underway and include new screening guidelines that incorporate considerations for patients aged 45 years, with unique features and new techniques at the forefront of screening. One of these new techniques is artificial intelligence which can increase adenoma detection rate and reduce the prevalence of colonic neoplasia.
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Affiliation(s)
- Georgios Zacharakis
- Division of Gastroenterology, Department of Internal Medicine, College of Medicine, Prince Sattam bin Abdulaziz University Hospital, Al Kharj 16277, Saudi Arabia
| | - Abdulaziz Almasoud
- Department of Gastroenterology and Hepatology, Prince Sultan Military Medical City, Riyadh 12233, Saudi Arabia
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24
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Kadomatsu Y, Nakao M, Ueno H, Nakamura S, Chen-Yoshikawa TF. A novel system applying artificial intelligence in the identification of air leak sites. JTCVS Tech 2022; 15:181-191. [PMID: 36276675 PMCID: PMC9579513 DOI: 10.1016/j.xjtc.2022.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/27/2022] [Accepted: 06/22/2022] [Indexed: 11/04/2022] Open
Abstract
Objective Prolonged air leak is the most common complication of thoracic surgery. Intraoperative leak site detection is the first step in decreasing the risk of leak-related postoperative complications. Methods We retrospectively reviewed the surgical videos of patients who underwent lung resection at our institution. In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. In the testing phase, a different data set was used to evaluate our proposed application for each predicted box. Results A total of 110 originally captured and labeled images obtained from 70 surgeries were preprocessed for the training data set. The testing data set contained 64 leak-positive and 45 leak-negative sites. The testing data set was obtained from 93 operations, including 58 patients in whom an air leak was present and 35 patients in whom an air leak was absent. In the testing phase, our software detected leak sites with a sensitivity and specificity of 81.3% and 68.9%, respectively. Conclusions We have successfully developed a deep learning-based leak site detection application, which can be used in deflated lungs. Although the current version is still a prototype with a limited training data set, it is a novel concept of leak detection based entirely on visual information.
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25
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Li YC, Chen HX, Xu WT, Li CK, Qi XS. Factors affecting colorectal adenoma detection rate. Shijie Huaren Xiaohua Zazhi 2022; 30:450-457. [DOI: 10.11569/wcjd.v30.i10.450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Affiliation(s)
- Ying-Chao Li
- Department of Gastroenterology, General Hospital of Northern Theater Command, Shenyang 110840, Liaoning Province, China,Graduate School of Dalian Medical University, Dalian 116044, Liaoning Province, China
| | - Hong-Xin Chen
- Department of Gastroenterology, General Hospital of Northern Theater Command, Shenyang 110840, Liaoning Province, China,Graduate School of Liaoning University of Traditional Chinese Medicine, Shenyang 110031, Liaoning Province, China
| | - Wen-Tao Xu
- Department of Gastroenterology, General Hospital of Northern Theater Command, Shenyang 110840, Liaoning Province, China,Postgraduate College, Shenyang Pharmaceutical University, Shenyang 110016, Liaoning Province, China
| | - Cheng-Kun Li
- Department of Gastroenterology, General Hospital of Northern Theater Command, Shenyang 110840, Liaoning Province, China
| | - Xing-Shun Qi
- Department of Gastroenterology, General Hospital of Northern Theater Command, Shenyang 110840, Liaoning Province, China
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26
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A deep ensemble learning method for colorectal polyp classification with optimized network parameters. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03689-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
AbstractColorectal Cancer (CRC), a leading cause of cancer-related deaths, can be abated by timely polypectomy. Computer-aided classification of polyps helps endoscopists to resect timely without submitting the sample for histology. Deep learning-based algorithms are promoted for computer-aided colorectal polyp classification. However, the existing methods do not accommodate any information on hyperparametric settings essential for model optimisation. Furthermore, unlike the polyp types, i.e., hyperplastic and adenomatous, the third type, serrated adenoma, is difficult to classify due to its hybrid nature. Moreover, automated assessment of polyps is a challenging task due to the similarities in their patterns; therefore, the strength of individual weak learners is combined to form a weighted ensemble model for an accurate classification model by establishing the optimised hyperparameters. In contrast to existing studies on binary classification, multiclass classification require evaluation through advanced measures. This study compared six existing Convolutional Neural Networks in addition to transfer learning and opted for optimum performing architecture only for ensemble models. The performance evaluation on UCI and PICCOLO dataset of the proposed method in terms of accuracy (96.3%, 81.2%), precision (95.5%, 82.4%), recall (97.2%, 81.1%), F1-score (96.3%, 81.3%) and model reliability using Cohen’s Kappa Coefficient (0.94, 0.62) shows the superiority over existing models. The outcomes of experiments by other studies on the same dataset yielded 82.5% accuracy with 72.7% recall by SVM and 85.9% accuracy with 87.6% recall by other deep learning methods. The proposed method demonstrates that a weighted ensemble of optimised networks along with data augmentation significantly boosts the performance of deep learning-based CAD.
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27
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Kutsumi H. Contribution of the Japan Gastroenterological Endoscopy Society to promote computer-aided diagnosis/detection system development using artificial intelligence technology. Dig Endosc 2022; 34 Suppl 2:132-135. [PMID: 34652003 DOI: 10.1111/den.14146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Hiromu Kutsumi
- Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan
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28
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Marques KF, Marques AF, Lopes MA, Beraldo RF, Lima TB, Sassaki LY. Artificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease. Artif Intell Gastrointest Endosc 2022; 3:1-8. [DOI: 10.37126/aige.v3.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/14/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
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29
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Yoon D, Kong HJ, Kim BS, Cho WS, Lee JC, Cho M, Lim MH, Yang SY, Lim SH, Lee J, Song JH, Chung GE, Choi JM, Kang HY, Bae JH, Kim S. Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network. Sci Rep 2022; 12:261. [PMID: 34997124 PMCID: PMC8741803 DOI: 10.1038/s41598-021-04247-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/20/2021] [Indexed: 12/28/2022] Open
Abstract
Computer-aided detection (CADe) systems have been actively researched for polyp detection in colonoscopy. To be an effective system, it is important to detect additional polyps that may be easily missed by endoscopists. Sessile serrated lesions (SSLs) are a precursor to colorectal cancer with a relatively higher miss rate, owing to their flat and subtle morphology. Colonoscopy CADe systems could help endoscopists; however, the current systems exhibit a very low performance for detecting SSLs. We propose a polyp detection system that reflects the morphological characteristics of SSLs to detect unrecognized or easily missed polyps. To develop a well-trained system with imbalanced polyp data, a generative adversarial network (GAN) was used to synthesize high-resolution whole endoscopic images, including SSL. Quantitative and qualitative evaluations on GAN-synthesized images ensure that synthetic images are realistic and include SSL endoscopic features. Moreover, traditional augmentation methods were used to compare the efficacy of the GAN augmentation method. The CADe system augmented with GAN synthesized images showed a 17.5% improvement in sensitivity on SSLs. Consequently, we verified the potential of the GAN to synthesize high-resolution images with endoscopic features and the proposed system was found to be effective in detecting easily missed polyps during a colonoscopy.
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Affiliation(s)
- Dan Yoon
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Hyoun-Joong Kong
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.,Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea
| | - Byeong Soo Kim
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Woo Sang Cho
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea
| | - Jung Chan Lee
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.,Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, South Korea.,Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea
| | - Minwoo Cho
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.,Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Min Hyuk Lim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea
| | - Sun Young Yang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Seon Hee Lim
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Jooyoung Lee
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Ji Hyun Song
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Goh Eun Chung
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Ji Min Choi
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Hae Yeon Kang
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea
| | - Jung Ho Bae
- Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
| | - Sungwan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea. .,Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea. .,Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea.
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30
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Abstract
Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People’ Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 People’s Republic of China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030 People’s Republic of China
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31
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Kandel P, Wallace MB. Advanced Imaging Techniques and In vivo Histology: Current Status and Future Perspectives (Lower G.I.). GASTROINTESTINAL AND PANCREATICO-BILIARY DISEASES: ADVANCED DIAGNOSTIC AND THERAPEUTIC ENDOSCOPY 2022:291-310. [DOI: 10.1007/978-3-030-56993-8_110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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32
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Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27:8103-8122. [PMID: 35068857 PMCID: PMC8704267 DOI: 10.3748/wjg.v27.i47.8103] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/22/2021] [Accepted: 12/03/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.
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Affiliation(s)
- Mahsa Taghiakbari
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo 0450, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Daniel von Renteln
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
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33
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Polyp Detection from Colorectum Images by Using Attentive YOLOv5. Diagnostics (Basel) 2021; 11:diagnostics11122264. [PMID: 34943501 PMCID: PMC8700704 DOI: 10.3390/diagnostics11122264] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 01/05/2023] Open
Abstract
Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.
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34
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Pfeifer L, Neufert C, Leppkes M, Waldner MJ, Häfner M, Beyer A, Hoffman A, Siersema PD, Neurath MF, Rath T. Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. Eur J Gastroenterol Hepatol 2021; 33:e662-e669. [PMID: 34034272 PMCID: PMC8734627 DOI: 10.1097/meg.0000000000002209] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 02/22/2021] [Indexed: 12/27/2022]
Abstract
AIM The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial. METHODS For training of the DCNN, 116 529 colonoscopy images from 278 patients with 788 different polyps were collected. A subset of 10 467 images containing 504 different polyps were manually annotated and treated as the gold standard. An independent set of 45 videos consisting of 15 534 single frames was used for ex vivo performance testing. In vivo real-time detection of colorectal polyps during routine colonoscopy by the DCNN was tested in 42 patients in a back-to-back approach. RESULTS When analyzing the test set of 15 534 single frames, the DCNN's sensitivity and specificity for polyp detection and localization within the frame was 90% and 80%, respectively, with an area under the curve of 0.92. In vivo, baseline polyp detection rate and ADR were 38% and 26% and significantly increased to 50% (P = 0.023) and 36% (P = 0.044), respectively, with the use of the DCNN. Of the 13 additionally with the DCNN detected lesions, the majority were diminutive and flat, among them three sessile serrated adenomas. CONCLUSION This newly developed DCNN enables highly sensitive automated detection of colorectal polyps both ex vivo and during first in-human clinical testing and could potentially increase the detection of colorectal polyps during colonoscopy.
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Affiliation(s)
- Lukas Pfeifer
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
| | - Clemens Neufert
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
| | - Moritz Leppkes
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
| | - Maximilian J. Waldner
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
| | - Michael Häfner
- Department of Gastroenterology, Physiopathology and Endoscopy of the Gastrointestinal Tract, Central Hospital Bolzano, Bolzano, Italy
| | | | - Arthur Hoffman
- Department of Internal Medicine 3, Division of Gastroenterology, Klinikum Aschaffenburg, Aschaffenburg, Germany
| | - Peter D. Siersema
- Department of Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Markus F. Neurath
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
| | - Timo Rath
- Department of Internal Medicine 1, Division of Gastroenterology, Ludwig Demling Endoscopy Center of Excellence, Friedrich-Alexander-University, Erlangen-Nuernberg, Germany
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35
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Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest Endosc 2021; 94:1099-1109.e10. [PMID: 34216598 DOI: 10.1016/j.gie.2021.06.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 06/22/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Colorectal cancer is a leading cause of death. Colonoscopy is the criterion standard for detection and removal of precancerous lesions and has been shown to reduce mortality. The polyp miss rate during colonoscopies is 22% to 28%. DEEP DEtection of Elusive Polyps (DEEP2) is a new polyp detection system based on deep learning that alerts the operator in real time to the presence and location of polyps. The primary outcome was the performance of DEEP2 on the detection of elusive polyps. METHODS The DEEP2 system was trained on 3611 hours of colonoscopy videos derived from 2 sources and was validated on a set comprising 1393 hours from a third unrelated source. Ground truth labeling was provided by offline gastroenterologist annotators who were able to watch the video in slow motion and pause and rewind as required. To assess applicability, stability, and user experience and to obtain some preliminary data on performance in a real-life scenario, a preliminary prospective clinical validation study was performed comprising 100 procedures. RESULTS DEEP2 achieved a sensitivity of 97.1% at 4.6 false alarms per video for all polyps and of 88.5% and 84.9% for polyps in the field of view for less than 5 and 2 seconds, respectively. DEEP2 was able to detect polyps not seen by live real-time endoscopists or offline annotators in an average of .22 polyps per sequence. In the clinical validation study, the system detected an average of .89 additional polyps per procedure. No adverse events occurred. CONCLUSIONS DEEP2 has a high sensitivity for polyp detection and was effective in increasing the detection of polyps both in colonoscopy videos and in real procedures with a low number of false alarms. (Clinical trial registration number: NCT04693078.).
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36
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Xu L, He X, Zhou J, Zhang J, Mao X, Ye G, Chen Q, Xu F, Sang J, Wang J, Ding Y, Li Y, Yu C. Artificial intelligence-assisted colonoscopy: A prospective, multicenter, randomized controlled trial of polyp detection. Cancer Med 2021; 10:7184-7193. [PMID: 34477306 PMCID: PMC8525182 DOI: 10.1002/cam4.4261] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 08/20/2021] [Accepted: 08/26/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) assistance has been considered as a promising way to improve colonoscopic polyp detection, but there are limited prospective studies on real-time use of AI systems. METHODS We conducted a prospective, multicenter, randomized controlled trial of patients undergoing colonoscopy at six centers. Eligible patients were randomly assigned to conventional colonoscopy (control group) or AI-assisted colonoscopy (AI group). AI assistance was our newly developed AI system for real-time colonoscopic polyp detection. Primary outcome is polyp detection rate (PDR). Secondary outcomes include polyps per positive patient (PPP), polyps per colonoscopy (PPC), and non-first polyps per colonoscopy (PPC-Plus). RESULTS A total of 2352 patients were included in the final analysis. Compared with the control, AI group did not show significant increment in PDR (38.8% vs. 36.2%, p = 0.183), but its PPC-Plus was significantly higher (0.5 vs. 0.4, p < 0.05). In addition, AI group detected more diminutive polyps (76.0% vs. 68.8%, p < 0.01) and flat polyps (5.9% vs. 3.3%, p < 0.05). The effects varied somewhat between centers. In further logistic regression analysis, AI assistance independently contributed to the increment of PDR, and the impact was more pronounced for male endoscopists, shorter insertion time but longer withdrawal time, and elderly patients with larger waist circumference. CONCLUSION The intervention of AI plays a limited role in overall polyp detection, but increases detection of easily missed polyps; ChiCTR.org.cn number, ChiCTR1800015607.
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Affiliation(s)
- Lei Xu
- Department of GastroenterologyNingbo Hospital of Zhejiang UniversityNingboChina
| | - Xinjue He
- Department of GastroenterologyThe First Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Jianbo Zhou
- Department of GastroenterologyYuyao People’s Hospital of Zhejiang ProvinceYuyaoChina
| | - Jie Zhang
- Department of GastroenterologyThe First Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Xinli Mao
- Department of GastroenterologyTaizhou Hospital of Zhejiang ProvinceLinhaiChina
| | - Guoliang Ye
- Department of GastroenterologyThe Affiliated Hospital of Medical School of Ningbo UniversityNingboChina
| | - Qiang Chen
- Department of GastroenterologySanmen People’s HospitalTaizhouChina
| | - Feng Xu
- Department of GastroenterologyNingbo Yinzhou People’s HospitalNingboChina
| | - Jianzhong Sang
- Department of GastroenterologyYuyao People’s Hospital of Zhejiang ProvinceYuyaoChina
| | - Jun Wang
- Department of GastroenterologyTaizhou Hospital of Zhejiang ProvinceLinhaiChina
| | - Yong Ding
- Department of GastroenterologyThe Affiliated Hospital of Medical School of Ningbo UniversityNingboChina
| | - Youming Li
- Department of GastroenterologyThe First Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
| | - Chaohui Yu
- Department of GastroenterologyThe First Affiliated Hospital, College of Medicine, Zhejiang UniversityHangzhouChina
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Ahmad OF, Mori Y, Misawa M, Kudo SE, Anderson JT, Bernal J, Berzin TM, Bisschops R, Byrne MF, Chen PJ, East JE, Eelbode T, Elson DS, Gurudu SR, Histace A, Karnes WE, Repici A, Singh R, Valdastri P, Wallace MB, Wang P, Stoyanov D, Lovat LB. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method. Endoscopy 2021; 53:893-901. [PMID: 33167043 PMCID: PMC8390295 DOI: 10.1055/a-1306-7590] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/09/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
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Affiliation(s)
- Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
- Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - John T. Anderson
- Department of Gastroenterology, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Jorge Bernal
- Computer Science Department, Universitat Autonoma de Barcelona and Computer Vision Center, Barcelona, Spain
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID KU Leuven, Leuven, Belgium
| | - Michael F. Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peng-Jen Chen
- Division of Gastroenterology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - James E. East
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, UK
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tom Eelbode
- Medical Imaging Research Center, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Daniel S. Elson
- Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Suryakanth R. Gurudu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Aymeric Histace
- ETIS, Universite de Cergy-Pointoise, ENSEA, CNRS, Cergy-Pointoise Cedex, France
| | - William E. Karnes
- H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology & Hepatology, Department of Medicine, University of California, Irvine, California, USA
| | - Alessandro Repici
- Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwan Hospital, Adelaide, South Australia, Australia
| | - Pietro Valdastri
- School of Electronics and Electrical Engineering, University of Leeds, Leeds, UK
| | - Michael B. Wallace
- Division of Gastroenterology & Hepatology, Mayo Clinic, Jacksonville, Florida, USA
| | - Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Laurence B. Lovat
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
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38
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Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27:4802-4817. [PMID: 34447227 PMCID: PMC8371500 DOI: 10.3748/wjg.v27.i29.4802] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/12/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.
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Affiliation(s)
- Joel Joseph
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Ella Marie LePage
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Catherine Phillips Cheney
- Department of Internal Medicine, Wake Forest School of Medicine, Winston Salem, NC 27157, United States
| | - Rishi Pawa
- Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, United States
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39
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Li J, Lu J, Yan J, Tan Y, Liu D. Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2021; 33:1041-1048. [PMID: 32804846 DOI: 10.1097/meg.0000000000001906] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Colonoscopy is an important method to diagnose polyps, especially adenomatous polyps. However, the rate of missed diagnoses is relatively high. In this study, we aimed to determine whether artificial intelligence (AI) improves the polyp detection rate (PDR) and adenoma detection rate (ADR) with colonoscopy. We performed a systematic search in PubMed, Cochrane Library, Embase, and Web of Science databases; the search included entries in the databases up to and including 29 February 2020. Five articles that involved a total of 4311 patients fulfilled the selection criteria. The results of these studies showed that both PDR and ADR increased with the assistance of AI compared with those in control groups {pooled odds ratio (OR) = 1.91 [95% confidence interval (CI) 1.68-2.16] and 1.75 (95% CI 1.52-2.01), respectively}. Good bowel preparation reduced the impact of AI, but significant differences were still apparent in PDR and ADR [pooled OR = 1.69 (95% CI 1.32-2.16) and 1.36 (95% CI 1.04-1.78), respectively]. The characteristics of polyps and adenomas also influenced the results. The average number of polyps and adenomas detected varied significantly by location, and small polyps and adenomas were more likely to be missed. However, the effect of the morphology of polyps and AI-assisted detection needs further studies. In conclusion, AI increases the detection rates of polyps and adenomas in colonoscopy. Without AI assistance, detection rates can be improved with better bowel preparation and training for small polyp and adenoma detection.
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Affiliation(s)
- Jianglei Li
- Department of Gastroenterology, The Second Xiangya Hospital
- Research Center of Digestive Disease, Central South University, Changsha, Hunan, P. R. China
| | - Jiaxi Lu
- Department of Gastroenterology, The Second Xiangya Hospital
- Research Center of Digestive Disease, Central South University, Changsha, Hunan, P. R. China
| | - Jin Yan
- Department of Gastroenterology, The Second Xiangya Hospital
- Research Center of Digestive Disease, Central South University, Changsha, Hunan, P. R. China
| | - Yuyong Tan
- Department of Gastroenterology, The Second Xiangya Hospital
- Research Center of Digestive Disease, Central South University, Changsha, Hunan, P. R. China
| | - Deliang Liu
- Department of Gastroenterology, The Second Xiangya Hospital
- Research Center of Digestive Disease, Central South University, Changsha, Hunan, P. R. China
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40
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:79-88. [DOI: 10.37126/aige.v2.i3.79] [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: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the major causes of death worldwide. Colonoscopy is the most important tool that can identify neoplastic lesion in early stages and resect it in a timely manner which helps in reducing mortality related to colorectal cancer. However, the quality of colonoscopy findings depends on the expertise of the endoscopist and thus the rate of missed adenoma or polyp cannot be controlled. It is desirable to standardize the quality of colonoscopy by reducing the number of missed adenoma/polyps. Introduction of artificial intelligence (AI) in the field of medicine has become popular among physicians nowadays. The application of AI in colonoscopy can help in reducing miss rate and increasing colorectal cancer detection rate as per recent studies. Moreover, AI assistance during colonoscopy has also been utilized in patients with inflammatory bowel disease to improve diagnostic accuracy, assessing disease severity and predicting clinical outcomes. We conducted a literature review on the available evidence on use of AI in colonoscopy. In this review article, we discuss about the principles, application, limitations, and future aspects of AI in colonoscopy.
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Affiliation(s)
- Niel Shah
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Abhilasha Jyala
- Department of Internal Medicine, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Harish Patel
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
| | - Jasbir Makker
- Department of Internal Medicine, Gastroenterology, BronxCare Hospital Center, Bronx, NY 10457, United States
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41
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Shah N, Jyala A, Patel H, Makker J. Utility of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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42
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Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2:69-76. [DOI: 10.35712/aig.v2.i3.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/09/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
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Lepore Signorile M, Disciglio V, Di Carlo G, Pisani A, Simone C, Ingravallo G. From Genetics to Histomolecular Characterization: An Insight into Colorectal Carcinogenesis in Lynch Syndrome. Int J Mol Sci 2021; 22:ijms22136767. [PMID: 34201893 PMCID: PMC8268977 DOI: 10.3390/ijms22136767] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/18/2021] [Accepted: 06/21/2021] [Indexed: 12/30/2022] Open
Abstract
Lynch syndrome is a hereditary cancer-predisposing syndrome caused by germline defects in DNA mismatch repair (MMR) genes such as MLH1, MSH2, MSH6, and PMS2. Carriers of pathogenic mutations in these genes have an increased lifetime risk of developing colorectal cancer (CRC) and other malignancies. Despite intensive surveillance, Lynch patients typically develop CRC after 10 years of follow-up, regardless of the screening interval. Recently, three different molecular models of colorectal carcinogenesis were identified in Lynch patients based on when MMR deficiency is acquired. In the first pathway, adenoma formation occurs in an MMR-proficient background, and carcinogenesis is characterized by APC and/or KRAS mutation and IGF2, NEUROG1, CDK2A, and/or CRABP1 hypermethylation. In the second pathway, deficiency in the MMR pathway is an early event arising in macroscopically normal gut surface before adenoma formation. In the third pathway, which is associated with mutations in CTNNB1 and/or TP53, the adenoma step is skipped, with fast and invasive tumor growth occurring in an MMR-deficient context. Here, we describe the association between molecular and histological features in these three routes of colorectal carcinogenesis in Lynch patients. The findings summarized in this review may guide the use of individualized surveillance guidelines based on a patient’s carcinogenesis subtype.
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Affiliation(s)
- Martina Lepore Signorile
- Medical Genetics, National Institute for Gastroenterology, IRCCS “S. de Bellis” Research Hospital, 70013 Castellana Grotte, Italy; (M.L.S.); (V.D.)
| | - Vittoria Disciglio
- Medical Genetics, National Institute for Gastroenterology, IRCCS “S. de Bellis” Research Hospital, 70013 Castellana Grotte, Italy; (M.L.S.); (V.D.)
| | - Gabriella Di Carlo
- Department of Emergency and Organ Transplantation, Section of Pathology, University of Bari Aldo Moro, 70124 Bari, Italy;
| | - Antonio Pisani
- Gastroenterology and Digestive Endoscopy Unit, National Institute for Gastroenterology, IRCCS “S. de Bellis” Research Hospital, 70013 Castellana Grotte, Italy;
| | - Cristiano Simone
- Medical Genetics, National Institute for Gastroenterology, IRCCS “S. de Bellis” Research Hospital, 70013 Castellana Grotte, Italy; (M.L.S.); (V.D.)
- Medical Genetics, Department of Biomedical Sciences and Human Oncology (DIMO), University of Bari Aldo Moro, 70124 Bari, Italy
- Correspondence: (C.S.); (G.I.)
| | - Giuseppe Ingravallo
- Department of Emergency and Organ Transplantation, Section of Pathology, University of Bari Aldo Moro, 70124 Bari, Italy;
- Correspondence: (C.S.); (G.I.)
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44
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Kim KO, Kim EY. Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm. Gut Liver 2021; 15:346-353. [PMID: 32773386 PMCID: PMC8129657 DOI: 10.5009/gnl20186] [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: 06/12/2020] [Accepted: 06/28/2020] [Indexed: 12/19/2022] Open
Abstract
Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)
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Affiliation(s)
- Kyeong Ok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Eun Young Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
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45
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Riemann JF, Teufel A, Ganslandt T, Hann A, Hildebrandt H, Jütte H, Meining A, Meyer H, Naumann A, Opitz O, Schilling D, Hüppe D. Digitale Kommunikationsstrategien in der Gastroenterologie. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2021; 59:473-474. [PMID: 34224118 DOI: 10.1055/a-1458-6430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | - Andreas Teufel
- II. Medizinische Klinik Sektionsleiter Hepatologie und Klinische Bioinformatik, Universitätsmedizin Mannheim
| | - Thomas Ganslandt
- Zentrum für Präventivmedizin und Digitale Gesundheit Baden-Württemberg, Medizinische Fakultät Mannheim der Universität Heidelberg
| | | | | | | | | | | | - Axel Naumann
- Praxiszentrum für Gastroenterologie und Endoskopie, Grevenbroich
| | - Oliver Opitz
- Medizinische Fakultät Mannheim der Ruprecht-Karls-Universität Heidelberg
| | - Dieter Schilling
- Diakonissenkrankenhaus, Theresienkrankenhaus, St. Hedwig-Klinik Mannheim
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Perrod G, Rahmi G, Cellier C. Colorectal cancer screening in Lynch syndrome: Indication, techniques and future perspectives. Dig Endosc 2021; 33:520-528. [PMID: 32314431 DOI: 10.1111/den.13702] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/04/2020] [Accepted: 04/14/2020] [Indexed: 12/15/2022]
Abstract
Lynch syndrome (LS) is an inherited predisposition to colorectal cancer (CRC), responsible for 3-5% of all CRC. This syndrome is characterized by the early occurrence of colorectal neoplastic lesions, with variable incidences depending on the type of pathogenic variants in MMR genes (MLH1, MSH2, MSH6, PMS2 and EPCAM) and demographics factors such as gender, body mass index, tobacco use and physical activity. Similar to sporadic cancers, colorectal screening by colonoscopy is efficient because it is associated with a reduction >50% of both CRC incidence and CRC related mortality. To that end, most guidelines recommend high definition screening colonoscopies in dedicated centers, starting at the age of 20-25 years old, with a surveillance interval of 1-2 years. In this review, we discuss the importance of high definition colonoscopies, including the compliance to specific key performance indicators, as well as the expected benefits of specific imaging modalities including virtual chromoendoscopy and dye-spray chromoendoscopy.
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Affiliation(s)
- Guillaume Perrod
- Hepato-gastroenterolgy and Digestive Endoscopy Department, Georges Pompidou European Hospital, APHP. Centre-Université de Paris, Paris, France.,PRED-IdF Network, Georges Pompidou European Hospital, Paris, France
| | - Gabriel Rahmi
- Hepato-gastroenterolgy and Digestive Endoscopy Department, Georges Pompidou European Hospital, APHP. Centre-Université de Paris, Paris, France.,PRED-IdF Network, Georges Pompidou European Hospital, Paris, France
| | - Christophe Cellier
- Hepato-gastroenterolgy and Digestive Endoscopy Department, Georges Pompidou European Hospital, APHP. Centre-Université de Paris, Paris, France.,PRED-IdF Network, Georges Pompidou European Hospital, Paris, France
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47
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Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2:36-49. [DOI: 10.37126/aige.v2.i2.36] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/27/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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48
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Hosotani K, Imai K, Hotta K, Ito S, Kishida Y, Yabuuchi Y, Yoshida M, Kawata N, Kakushima N, Takizawa K, Ishiwatari H, Matsubayashi H, Ono H. Diagnostic performance for T1 cancer in colorectal lesions ≥10 mm by optical characterization using magnifying narrow-band imaging combined with magnifying chromoendoscopy; implications for optimized stratification by Japan Narrow-band Imaging Expert Team classification. Dig Endosc 2021; 33:425-432. [PMID: 32530105 DOI: 10.1111/den.13766] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/05/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND Magnifying narrow-band imaging (M-NBI) and magnifying chromoendoscopy (M-CE) enable accurate diagnosis of T1 colorectal cancer, but the diagnostic yields from combined M-NBI and CE have not been fully analyzed. We aimed to evaluate the diagnostic yield of combining Japan NBI Expert Team (JNET) classification using M-NBI and M-CE. METHODS Superficial colorectal lesions ≥10 mm removed at a Japanese tertiary cancer center between February 2016 and December 2018 were included. We analyzed the relationship between JNET classification, M-CE findings, and histological results based on prospectively collected endoscopic and pathologic data. RESULTS A total of 1573 lesions, including 56 superficial submucosal invasive cancers, 160 deep submucosal invasive cancers, and 81 advanced cancers (≥T2) were analyzed. The probability of deeply invasive cancer (95% confidence interval) was 1.8% (1.1-2.8), 30.1% (25.4-35.1), and 96.6% (91.5-99.1) in JNET Types 2A, 2B, and 3, respectively. The probability of deeply invasive cancer in JNET Type 2B lesions with non-V, VL, and VH pit pattern was 4.3%, 16.6%, 76.0%, respectively (P < 0.001). CONCLUSIONS Our study showed the stratification by M-NBI using JNET classification and the effect of additional M-CE for JNET Type 2B lesions.
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Affiliation(s)
- Kazuya Hosotani
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kenichiro Imai
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Sayo Ito
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | - Yohei Yabuuchi
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masao Yoshida
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Noboru Kawata
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Naomi Kakushima
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kohei Takizawa
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | | | | | - Hiroyuki Ono
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
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50
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Uraoka T, Tanaka S, Saito Y, Matsumoto T, Kuribayashi S, Hori K, Tajiri H. Computer-assisted detection of diminutive and small colon polyps by colonoscopy using an extra-wide-area-view colonoscope. Endoscopy 2021; 53:E102-E103. [PMID: 32659811 DOI: 10.1055/a-1202-1277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Toshio Uraoka
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Shinji Tanaka
- Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Takayuki Matsumoto
- Division of Gastroenterology, Department of Medicine, Iwate Medical University, Morioka, Japan
| | - Shiko Kuribayashi
- Department of Gastroenterology and Hepatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Keisuke Hori
- Department of Internal Medicine, Tsuyama Central Hospital, Okayama, Japan
| | - Hisao Tajiri
- Department of Innovative Interventional Endoscopy Research, The Jikei University School of Medicine, Tokyo, Japan
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