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Huang C, Song Y, Dong J, Yang F, Guo J, Sun S. Diagnostic performance of AI-assisted endoscopy diagnosis of digestive system tumors: an umbrella review. Front Oncol 2025; 15:1519144. [PMID: 40248201 PMCID: PMC12003149 DOI: 10.3389/fonc.2025.1519144] [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: 10/29/2024] [Accepted: 03/18/2025] [Indexed: 04/19/2025] Open
Abstract
The diagnostic performance of artificial intelligence (AI)-assisted endoscopy for digestive tumors remains controversial. The objective of this umbrella review was to summarize the comprehensive evidence for the AI-assisted endoscopic diagnosis of digestive system tumors. We grouped the evidence according to the location of each digestive system tumor and performed separate subgroup analyses on the basis of the method of data collection and form of the data. We also compared the diagnostic performance of AI with that of experts and nonexperts. For early digestive system cancer and precancerous lesions, AI showed a high diagnostic performance in capsule endoscopy and esophageal squamous cell carcinoma. Additionally, AI-assisted endoscopic ultrasonography (EUS) had good diagnostic accuracy for pancreatic cancer. In the subgroup analysis, AI had a better diagnostic performance than experts for most digestive system tumors. However, the diagnostic performance of AI using video data requires improvement.
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Affiliation(s)
- Changwei Huang
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yue Song
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jize Dong
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Fan Yang
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Jintao Guo
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Engineering Research Center of Ministry of Education for Minimally Invasive Gastrointestinal Endoscopic Techniques, Shenyang, Liaoning, China
| | - Siyu Sun
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- Engineering Research Center of Ministry of Education for Minimally Invasive Gastrointestinal Endoscopic Techniques, Shenyang, Liaoning, China
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Xu HL, Gong TT, Song XJ, Chen Q, Bao Q, Yao W, Xie MM, Li C, Grzegorzek M, Shi Y, Sun HZ, Li XH, Zhao YH, Gao S, Wu QJ. Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews. J Med Internet Res 2025; 27:e53567. [PMID: 40167239 PMCID: PMC12000792 DOI: 10.2196/53567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 07/30/2024] [Accepted: 11/11/2024] [Indexed: 04/02/2025] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading to better patient outcomes. OBJECTIVE We performed an umbrella review to summarize and critically evaluate the evidence for the AI-based imaging diagnosis of cancers. METHODS PubMed, Embase, Web of Science, Cochrane, and IEEE databases were searched for relevant systematic reviews from inception to June 19, 2024. Two independent investigators abstracted data and assessed the quality of evidence, using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence in each meta-analysis by applying the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. Diagnostic performance data were synthesized narratively. RESULTS In a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). Similarities were observed in the diagnostic performance for cancers of the head and neck, respiratory system, digestive system, urinary system, female-related systems, skin, and other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 reviews meta-analyzed sensitivity and specificity for esophageal cancer, showing ranges of 90%-95% and 80%-93.8%, respectively. In the case of breast cancer detection, 8 reviews calculated the pooled sensitivity and specificity within the ranges of 75.4%-92% and 83%-90.6%, respectively. Four meta-analyses reported the ranges of sensitivity and specificity in ovarian cancer, and both were 75%-94%. Notably, in lung cancer, the pooled specificity was relatively low, primarily distributed between 65% and 80%. Furthermore, 80.4% (127/158) of the included studies were of high quality according to the JBI Critical Appraisal Checklist, with the remaining studies classified as medium quality. The GRADE assessment indicated that the overall quality of the evidence was moderate to low. CONCLUSIONS Although AI shows great potential for achieving accelerated, accurate, and more objective diagnoses of multiple cancers, there are still hurdles to overcome before its implementation in clinical settings. The present findings highlight that a concerted effort from the research community, clinicians, and policymakers is required to overcome existing hurdles and translate this potential into improved patient outcomes and health care delivery. TRIAL REGISTRATION PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278.
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xin-Jian Song
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Chen
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi Bao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Wei Yao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Meng-Meng Xie
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiao-Han Li
- Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qi-Jun Wu
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
- NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China
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Yan L, He Q, Peng X, Lin S, Sha M, Zhao S, Huang D, Ye J. Prevalence of Helicobacter pylori infection in the general population in Wuzhou, China: a cross-sectional study. Infect Agent Cancer 2025; 20:1. [PMID: 39780274 PMCID: PMC11715292 DOI: 10.1186/s13027-024-00632-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2024] [Accepted: 12/23/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND Helicobacter pylori (H. pylori) is a global infectious carcinogen. We aimed to evaluate the prevalence of H. pylori infection in the healthcare-utilizing population undergoing physical examinations at a tertiary hospital in Guangxi, China. Furthermore, gastroscopies were performed on selected participants to scrutinize the endoscopic features of H. pylori infection among asymptomatic individuals. SUBJECTS AND METHODS This study involved 22,769 participants who underwent H. pylori antibody serology screenings at the hospital between 2020 and 2023. The 14C-urea breath test was employed to determine the current H. pylori infection status of 19,307 individuals. Concurrently, 293 participants underwent gastroscopy to evaluate their endoscopic mucosal abnormalities. The risk correlation and predictive value of endoscopic mucosal traits, Hp infection status, and 14C-urea breath test(14C-UBT) outcomes were investigated in subsequent analyses. RESULTS Serum Ure, CagA, and VacA antibodies were detected in 43.3%, 27.4%, and 23.6% of the 22,769 subjects that were screened, respectively. The population exhibited 27.5% and 17.2% positive rates for immune type I and II, respectively. Male participants exhibited lower positive rates of serum antibodies than females. The positive rates and predictive risks of the antibodies increased with age, and the highest positive rates were observed in the 50-60 age subgroup. Based on the outcomes of serological diagnostic techniques, it was observed that the positive rate was significantly higher compared to that of non-serological diagnostic methods, specifically the 14C-UBT results (43.3% versus 14.97%). Among the other cohort (n = 19,307), the 14C-UBT revealed a 14.97% positivity rate correlated with age. The 293 individuals who underwent gastroscopy from 14C-UBT Cohort were found to be at an increased risk of a positive breath test if they exhibited duodenal bulb inflammation, diffuse redness, or mucosal edema during the gastroscopy visit. CONCLUSION The prevalence of Helicobacter pylori infection is high among the population of Wuzhou, Guangxi, China. Type I H. pylori strains, distinguished by their enhanced virulence, are predominant in this region. In the framework of this population-based study, age has been identified as an independent risk factor for H. pylori infection. Additionally, distinct mucosal manifestations observed during gastroscopy can facilitate the identification of healthcare-utilizing individuals with active H. pylori infections.
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Affiliation(s)
- Liumei Yan
- Department of Gastroenterology and Gastrointestinal Endoscopy, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
- Affiliated Wuzhou Red Cross Hospital, Wuzhou Medical College, Wuzhou, Guangxi, 543199, China
| | - Qiliang He
- Health Management Center, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
| | - Xinyun Peng
- Clinical Laboratory, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
| | - Sen Lin
- Department of Information Technology, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
| | - Meigu Sha
- Health Management Center, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
| | - Shujian Zhao
- Clinical Laboratory, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China
| | - Dewang Huang
- Department of Gastroenterology and Gastrointestinal Endoscopy, Wuzhou Red Cross Hospital, Wuzhou, Guangxi, 543002, China.
| | - Jiemei Ye
- Affiliated Wuzhou Red Cross Hospital, Wuzhou Medical College, Wuzhou, Guangxi, 543199, China.
- Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Nanning, Guangxi, 530021, China.
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Fan C, Miao X, Sun X, Zhong Y, Liu B, Xiang M, Ye B. Current Status and Future Directions of Research on Artificial Intelligence in Nasopharyngolaryngoscopy. Respiration 2024; 104:255-263. [PMID: 39622215 DOI: 10.1159/000542362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 10/21/2024] [Indexed: 01/15/2025] Open
Abstract
BACKGROUND The nasopharyngolaryngoscopy (NPL) has emerged as a valuable tool for detecting early cases of head and neck cancers. However, misdiagnoses and missed diagnoses are still common phenomena. The expertise of examining physicians often serves as the primary limiting factor, leading to issues such as incomplete visualization, imprecise identification, and unclear vision. Over recent years, the application of artificial intelligence (AI) in medical imaging, particularly in the realm of gastrointestinal endoscopy, has instigated revolutionary changes in site quality control, lesion identification, and report generation. However, there remains a lack of standardized guidelines for the proper application of NPL across various countries. SUMMARY In this paper, we set our sights on reviewing the current clinical applications and summarizing the primary shortcomings of NPL. In addition, we encapsulate the progress of AI application within gastrointestinal endoscopy and NPL. Drawing from real-world clinical practice, we propose future directions and prospects for AI research in NPL. We firmly believe that the pace of clinical application of AI in NPL will accelerate significantly in the near future. KEY MESSAGES Incomplete examination coverage, failure to detect and diagnose lesions, and poor image quality happens in the current use of NPL. Currently, NPL examinations lack third-party supervision and quality control. AI application has achieved great advancements in gastrointestinal endoscopy concerning endoscopic quality control, lesion identification, and standardized reporting. While AI-related research in NPL is still in its nascent stages, it shows substantial potential for clinical application and endoscopic training. The interaction of AI into NPL examinations is potential and inevitable in the era of big data.
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Affiliation(s)
- Cui Fan
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China,
| | - Xiangwan Miao
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Xingmei Sun
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Yiming Zhong
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Bin Liu
- Endovista Information Technology Company Limited, Shanghai, China
| | - Mingliang Xiang
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Bin Ye
- Department of Otolaryngology & Head and Neck Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
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Shiha MG, Hadjisavvas N, Sanders DS, Penny HA. Optimising the Diagnosis of Adult Coeliac Disease: Current Evidence and Future Directions. Br J Hosp Med (Lond) 2024; 85:1-21. [PMID: 39347683 DOI: 10.12968/hmed.2024.0362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Coeliac disease is a common autoimmune disorder that affects nearly 1% of the general population. Current diagnostic strategies involve active case finding, serological tests, and endoscopy with biopsies. However, many patients with coeliac disease remain undiagnosed due to a wide gap between clinical guidelines and real-world practice in the diagnosis of adult coeliac disease. This highlights the need for increased education, training, and targeted quality-improvement interventions to optimise the diagnosis of coeliac disease.
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Affiliation(s)
- Mohamed G Shiha
- Academic Unit of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | | | - David S Sanders
- Academic Unit of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
| | - Hugo A Penny
- Academic Unit of Gastroenterology, Sheffield Teaching Hospitals, Sheffield, UK
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK
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Sugimoto M, Murata M, Murakami K, Yamaoka Y, Kawai T. Characteristic endoscopic findings in Helicobacter pylori diagnosis in clinical practice. Expert Rev Gastroenterol Hepatol 2024; 18:457-472. [PMID: 39162811 DOI: 10.1080/17474124.2024.2395317] [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: 04/10/2024] [Accepted: 08/19/2024] [Indexed: 08/21/2024]
Abstract
INTRODUCTION Helicobacter pylori is a major risk factor for gastric cancer. In addition to eradication therapy, early-phase detection of gastric cancer through screening programs using high-vision endoscopy is also widely known to reduce mortality. Although European and US guidelines recommend evaluation of atrophy and intestinal metaplasia by high-vision endoscopy and pathological findings, the guideline used in Japan - the Kyoto classification of gastritis - is based on endoscopic evaluation, and recommends the grading of risk factors. This system requires classification into three endoscopic groups: H. pylori-negative, previous H. pylori infection (inactive gastritis), and current H. pylori infection (active gastritis). Major endoscopic findings in active gastritis are diffuse redness, enlarged folds, nodularity, mucosal swelling, and sticky mucus, while those in H pylori-related gastritis - irrespective of active or inactive status - are atrophy, intestinal metaplasia, and xanthoma. AREAS COVERED This review describes the endoscopic characteristics of current H. pylori infection, and how characteristic endoscopic findings should be evaluated. EXPERT OPINION Although the correct evaluation of endoscopic findings related to H. pylori remains necessary, if findings of possible infection are observed, it is important to diagnose infection by detection methods with high sensitivity and specificity, including the stool antigen test and urea breath test.
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Affiliation(s)
- Mitsushige Sugimoto
- Division of Genome-Wide Infectious Diseases, Research Center for GLOBAL and LOCAL Infectious Disease, Oita University, Yufu, Japan
| | - Masaki Murata
- Department of Gastroenterology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | | | - Yoshio Yamaoka
- Division of Genome-Wide Infectious Diseases, Research Center for GLOBAL and LOCAL Infectious Disease, Oita University, Yufu, Japan
- Department of Environmental and Preventive Medicine, Oita University, Yufu, Japan
| | - Takashi Kawai
- Department of Gastroenterological Endoscopy, Tokyo Medical University Hospital, Shinjuku, Japan
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Zha B, Cai A, Wang G. Diagnostic Accuracy of Artificial Intelligence in Endoscopy: Umbrella Review. JMIR Med Inform 2024; 12:e56361. [PMID: 39093715 PMCID: PMC11296324 DOI: 10.2196/56361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 05/25/2024] [Accepted: 05/26/2024] [Indexed: 08/04/2024] Open
Abstract
Background Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. Objective This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy. Methods Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. Results A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. Conclusions AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness.
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Affiliation(s)
- Bowen Zha
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Angshu Cai
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Guiqi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Ligato I, De Magistris G, Dilaghi E, Cozza G, Ciardiello A, Panzuto F, Giagu S, Annibale B, Napoli C, Esposito G. Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches. Diagnostics (Basel) 2024; 14:1376. [PMID: 39001267 PMCID: PMC11241412 DOI: 10.3390/diagnostics14131376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/23/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.
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Affiliation(s)
- Irene Ligato
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Giorgio De Magistris
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy; (G.D.M.); (C.N.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Giulio Cozza
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Andrea Ciardiello
- Department of Physics, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy; (A.C.); (S.G.)
| | - Francesco Panzuto
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Stefano Giagu
- Department of Physics, Sapienza University of Rome, P.le A. Moro 5, 00185 Rome, Italy; (A.C.); (S.G.)
| | - Bruno Annibale
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
| | - Christian Napoli
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy; (G.D.M.); (C.N.)
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00185 Roma, Italy; (I.L.); (E.D.); (G.C.); (F.P.); (B.A.)
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Lee JG, Yoo IK, Yeniova AO, Lee SP, The Research Group for Endoscopic Imaging of Korean Society of Gastrointestinal Endoscopy. The Diagnostic Performance of Linked Color Imaging Compared to White Light Imaging in Endoscopic Diagnosis of Helicobacter pylori Infection: A Systematic Review and Meta-Analysis. Gut Liver 2024; 18:444-456. [PMID: 37800315 PMCID: PMC11096912 DOI: 10.5009/gnl230244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/25/2023] [Accepted: 08/02/2023] [Indexed: 10/07/2023] Open
Abstract
Background/Aims Recognizing Helicobacter pylori infection during endoscopy is important because it can lead to the performance of confirmatory testing. Linked color imaging (LCI) is an image enhancement technique that can improve the detection of gastrointestinal lesions. The purpose of this study was to compare LCI to conventional white light imaging (WLI) in the endoscopic diagnosis of H. pylori infection. Methods We conducted a comprehensive literature search using PubMed, Embase, and the Cochrane Library. All studies evaluating the diagnostic performance of LCI or WLI in the endoscopic diagnosis of H. pylori were eligible. Studies on magnifying endoscopy, chromoendoscopy, and artificial intelligence were excluded. Results Thirty-four studies were included in this meta-analysis, of which 32 reported the performance of WLI and eight reported the performance of LCI in diagnosing H. pylori infection. The pooled sensitivity and specificity of WLI in the diagnosis of H. pylori infection were 0.528 (95% confidence interval [CI], 0.517 to 0.540) and 0.821 (95% CI, 0.811 to 0.830), respectively. The pooled sensitivity and specificity of LCI in the diagnosis of H. pylori were 0.816 (95% CI, 0.790 to 0.841) and 0.868 (95% CI, 0.850 to 0.884), respectively. The pooled diagnostic odds ratios of WLI and LCI were 15.447 (95% CI, 8.225 to 29.013) and 31.838 (95% CI, 15.576 to 65.078), respectively. The areas under the summary receiver operating characteristic curves of WLI and LCI were 0.870 and 0.911, respectively. Conclusions LCI showed higher sensitivity in the endoscopic diagnosis of H. pylori infection than standard WLI.
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Affiliation(s)
- Jae Gon Lee
- Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
| | - In Kyung Yoo
- Department of Gastroenterology, CHA Bundang Medical Center, CHA University College of Medicine, Seongnam, Korea
| | - Abdullah Ozgur Yeniova
- Division of Gastroenterology, Department of Internal Medicine, Tokat Gaziosmanpasa University School of Medicine, Tokat, Turkey
| | - Sang Pyo Lee
- Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, Korea
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Lightdale CJ, Tiscornia-Wasserman P, Sethi A, Abrams JA, Laszkowska M, Dua A, Kim J, Soroush A, Zylberberg HM, Nathanson JT, Hur C. Endoscopy-Guided High-Pressure Spray “Power-Wash” for Detection of Gastric Intestinal Metaplasia and Dysplasia. TECHNIQUES AND INNOVATIONS IN GASTROINTESTINAL ENDOSCOPY 2024; 26:94-98. [DOI: 10.1016/j.tige.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2025]
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11
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Dao HV, Nguyen BP, Nguyen TT, Lam HN, Nguyen TTH, Dang TT, Hoang LB, Le HQ, Dao LV. Application of artificial intelligence in gastrointestinal endoscopy in Vietnam: a narrative review. Ther Adv Gastrointest Endosc 2024; 17:26317745241306562. [PMID: 39734422 PMCID: PMC11672465 DOI: 10.1177/26317745241306562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 11/25/2024] [Indexed: 12/31/2024] Open
Abstract
The utilization of artificial intelligence (AI) in gastrointestinal (GI) endoscopy has witnessed significant progress and promising results in recent years worldwide. From 2019 to 2023, the European Society of Gastrointestinal Endoscopy has released multiple guidelines/consensus with recommendations on integrating AI for detecting and classifying lesions in practical endoscopy. In Vietnam, since 2019, several preliminary studies have been conducted to develop AI algorithms for GI endoscopy, focusing on lesion detection. These studies have yielded high accuracy results ranging from 86% to 92%. For upper GI endoscopy, ongoing research directions comprise image quality assessment, detection of anatomical landmarks, simulating image-enhanced endoscopy, and semi-automated tools supporting the delineation of GI lesions on endoscopic images. For lower GI endoscopy, most studies focus on developing AI algorithms for colorectal polyps' detection and classification based on the risk of malignancy. In conclusion, the application of AI in this field represents a promising research direction, presenting challenges and opportunities for real-world implementation within the Vietnamese healthcare context.
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Affiliation(s)
- Hang Viet Dao
- Research and Education Department, Institute of Gastroenterology and Hepatology, 09 Dao Duy Anh Street, Dong Da District, Hanoi City, Vietnam
- Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam
- Endoscopy Center, Hanoi Medical University Hospital, Hanoi, Vietnam
| | | | | | - Hoa Ngoc Lam
- Institute of Gastroenterology and Hepatology, Hanoi, Vietnam
| | | | - Thao Thi Dang
- Institute of Gastroenterology and Hepatology, Hanoi, Vietnam
| | - Long Bao Hoang
- Institute of Gastroenterology and Hepatology, Hanoi, Vietnam
| | - Hung Quang Le
- Endoscopy Center, Hanoi Medical University Hospital, Hanoi, Vietnam
| | - Long Van Dao
- Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam
- Endoscopy Center, Hanoi Medical University Hospital, Hanoi, Vietnam
- Institute of Gastroenterology and Hepatology, Hanoi, Vietnam
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12
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Ishikawa Y, Sugino T, Okubo K, Nakajima Y. Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks. Surg Today 2023; 53:1380-1387. [PMID: 37354240 DOI: 10.1007/s00595-023-02708-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 04/03/2023] [Indexed: 06/26/2023]
Abstract
OBJECTIVES The prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer surgery. We conducted this study to analyze the efficacy of a deep convolutional neural network (DCNN) for tumor detection using recorded thoracoscopic images of pulmonary surfaces. MATERIALS AND METHODS We collected 644 intraoperative thoracoscopic images of changes in pulmonary appearance from 427 patients with lung cancer between 2012 and 2021. The lesion areas on the thoracoscopic images were detected by bounding boxes using an advanced version of YOLO, a well-known DCNN for object detection. The DCNN model was trained and evaluated by a 15-fold cross-validation scheme. Each predicted bounding box was considered successful detection when it overlapped more than 50% of the lesion areas annotated by board-certified surgeons. RESULTS AND CONCLUSIONS Precision, recall, and F1-measured values of 91.9%, 90.5%, and 91.1%, respectively, were obtained. The presence of lymphatic vessel invasion was associated with successful detection (p = 0.045). The presence of pathological pleural invasion also showed a tendency toward successful detection (p = 0.081). The proposed DCNN-based algorithm yielded an accuracy of more than 90% tumor detection. These algorithms will help surgeons detect lung cancer displayed on a screen automatically.
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Affiliation(s)
- Yuya Ishikawa
- Department of Thoracic Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takaaki Sugino
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, 2-3-10, Surugadai, Chiyoda-ku, Tokyo, 101-0062, Japan
| | - Kenichi Okubo
- Department of Thoracic Surgery, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoshikazu Nakajima
- Department of Biomedical Information, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, 2-3-10, Surugadai, Chiyoda-ku, Tokyo, 101-0062, Japan.
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13
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Weusten BLAM, Bisschops R, Dinis-Ribeiro M, di Pietro M, Pech O, Spaander MCW, Baldaque-Silva F, Barret M, Coron E, Fernández-Esparrach G, Fitzgerald RC, Jansen M, Jovani M, Marques-de-Sa I, Rattan A, Tan WK, Verheij EPD, Zellenrath PA, Triantafyllou K, Pouw RE. Diagnosis and management of Barrett esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Guideline. Endoscopy 2023; 55:1124-1146. [PMID: 37813356 DOI: 10.1055/a-2176-2440] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
MR1 : ESGE recommends the following standards for Barrett esophagus (BE) surveillance:- a minimum of 1-minute inspection time per cm of BE length during a surveillance endoscopy- photodocumentation of landmarks, the BE segment including one picture per cm of BE length, and the esophagogastric junction in retroflexed position, and any visible lesions- use of the Prague and (for visible lesions) Paris classification- collection of biopsies from all visible abnormalities (if present), followed by random four-quadrant biopsies for every 2-cm BE length.Strong recommendation, weak quality of evidence. MR2: ESGE suggests varying surveillance intervals for different BE lengths. For BE with a maximum extent of ≥ 1 cm and < 3 cm, BE surveillance should be repeated every 5 years. For BE with a maximum extent of ≥ 3 cm and < 10 cm, the interval for endoscopic surveillance should be 3 years. Patients with BE with a maximum extent of ≥ 10 cm should be referred to a BE expert center for surveillance endoscopies. For patients with an irregular Z-line/columnar-lined esophagus of < 1 cm, no routine biopsies or endoscopic surveillance are advised.Weak recommendation, low quality of evidence. MR3: ESGE suggests that, if a patient has reached 75 years of age at the time of the last surveillance endoscopy and/or the patient's life expectancy is less than 5 years, the discontinuation of further surveillance endoscopies can be considered. Weak recommendation, very low quality of evidence. MR4: ESGE recommends offering endoscopic eradication therapy using ablation to patients with BE and low grade dysplasia (LGD) on at least two separate endoscopies, both confirmed by a second experienced pathologist.Strong recommendation, high level of evidence. MR5: ESGE recommends endoscopic ablation treatment for BE with confirmed high grade dysplasia (HGD) without visible lesions, to prevent progression to invasive cancer.Strong recommendation, high level of evidence. MR6: ESGE recommends offering complete eradication of all remaining Barrett epithelium by ablation after endoscopic resection of visible abnormalities containing any degree of dysplasia or esophageal adenocarcinoma (EAC).Strong recommendation, moderate quality of evidence. MR7: ESGE recommends endoscopic resection as curative treatment for T1a Barrett's cancer with well/moderate differentiation and no signs of lymphovascular invasion.Strong recommendation, high level of evidence. MR8: ESGE suggests that low risk submucosal (T1b) EAC (i. e. submucosal invasion depth ≤ 500 µm AND no [lympho]vascular invasion AND no poor tumor differentiation) can be treated by endoscopic resection, provided that adequate follow-up with gastroscopy, endoscopic ultrasound (EUS), and computed tomography (CT)/positrion emission tomography-computed tomography (PET-CT) is performed in expert centers.Weak recommendation, low quality of evidence. MR9: ESGE suggests that submucosal (T1b) esophageal adenocarcinoma with deep submucosal invasion (tumor invasion > 500 µm into the submucosa), and/or (lympho)vascular invasion, and/or a poor tumor differentiation should be considered high risk. Complete staging and consideration of additional treatments (chemotherapy and/or radiotherapy and/or surgery) or strict endoscopic follow-up should be undertaken on an individual basis in a multidisciplinary discussion.Strong recommendation, low quality of evidence. MR10 A: ESGE recommends that the first endoscopic follow-up after successful endoscopic eradication therapy (EET) of BE is performed in an expert center.Strong recommendation, very low quality of evidence. B: ESGE recommends careful inspection of the neo-squamocolumnar junction and neo-squamous epithelium with high definition white-light endoscopy and virtual chromoendoscopy during post-EET surveillance, to detect recurrent dysplasia.Strong recommendation, very low level of evidence. C: ESGE recommends against routine four-quadrant biopsies of neo-squamous epithelium after successful EET of BE.Strong recommendation, low level of evidence. D: ESGE suggests, after successful EET, obtaining four-quadrant random biopsies just distal to a normal-appearing neo-squamocolumnar junction to detect dysplasia in the absence of visible lesions.Weak recommendation, low level of evidence. E: ESGE recommends targeted biopsies are obtained where there is a suspicion of recurrent BE in the tubular esophagus, or where there are visible lesions suspicious for dysplasia.Strong recommendation, very low level of evidence. MR11: After successful EET, ESGE recommends the following surveillance intervals:- For patients with a baseline diagnosis of HGD or EAC:at 1, 2, 3, 4, 5, 7, and 10 years after last treatment, after which surveillance may be stopped.- For patients with a baseline diagnosis of LGD:at 1, 3, and 5 years after last treatment, after which surveillance may be stopped.Strong recommendation, low quality of evidence.
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Affiliation(s)
- Bas L A M Weusten
- Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Department of Gastroenterology and Hepatology, St. Antonius Hospital Nieuwegein, Nieuwegein, The Netherlands
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID, Leuven, Belgium
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto Portugal
| | - Massimiliano di Pietro
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Oliver Pech
- Department of Gastroenterology and Interventional Endoscopy, St. John of God Hospital, Regensburg, Germany
| | - Manon C W Spaander
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Francisco Baldaque-Silva
- Advanced Endoscopy Center Carlos Moreira da Silva, Department of Gastroenterology, Pedro Hispano Hospital, Matosinhos, Portugal
- Division of Medicine, Department of Upper Gastrointestinal Diseases, Karolinska University Hospital and Karolinska Institute, Stockholm, Sweden
| | - Maximilien Barret
- Department of Gastroenterology and Digestive Oncology, Cochin Hospital and University of Paris, Paris, France
| | - Emmanuel Coron
- Institut des Maladies de l'Appareil Digestif, IMAD, Centre hospitalier universitaire Hôtel-Dieu, Nantes, Nantes, France
- Department of Gastroenterology and Hepatology, University Hospital of Geneva (HUG), Geneva, Switzerland
| | - Glòria Fernández-Esparrach
- Endoscopy Unit, Department of Gastroenterology, Hospital Clínic of Barcelona, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Biomedical Research Network on Hepatic and Digestive Diseases (CIBEREHD), Barcelona, Spain
| | - Rebecca C Fitzgerald
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Marnix Jansen
- Department of Histopathology, University College London Hospital NHS Trust, London, UK
| | - Manol Jovani
- Division of Gastroenterology, Maimonides Medical Center, New York, New York, USA
| | - Ines Marques-de-Sa
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto Portugal
| | - Arti Rattan
- Department of Gastroenterology, Wollongong Hospital, Wollongong, New South Wales, Australia
| | - W Keith Tan
- Early Cancer Institute, University of Cambridge and Department of Gastroenterology, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Eva P D Verheij
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam Gastroenterology, Endocrinology and Metabolism, Cancer Center Amsterdam, Amsterdam, The Netherlands
| | - Pauline A Zellenrath
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens, Greece
| | - Roos E Pouw
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers location University of Amsterdam, Amsterdam Gastroenterology, Endocrinology and Metabolism, Cancer Center Amsterdam, Amsterdam, The Netherlands
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Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15:1998-2016. [DOI: 10.4251/wjgo.v15.i11.1998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/05/2023] [Accepted: 10/11/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.
AIM To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).
METHODS PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
RESULTS A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).
CONCLUSION CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.
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Affiliation(s)
- Jun-Qi Zhang
- The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
| | - Jun-Jie Mi
- Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
| | - Rong Wang
- Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
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15
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Chang H, Choi JY, Shim J, Kim M, Choi M. Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records. Healthc Inform Res 2023; 29:323-333. [PMID: 37964454 PMCID: PMC10651408 DOI: 10.4258/hir.2023.29.4.323] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 10/20/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVES Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. METHODS The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. RESULTS Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. CONCLUSIONS Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.
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Affiliation(s)
- Hyejung Chang
- Department of Management, School of Management, Kyung Hee University, Seoul,
Korea
| | - Jae-Young Choi
- Department of Business Administration, College of Business, Hallym University, Chuncheon,
Korea
| | - Jaesun Shim
- Department of Municipal Hospital Policy & Management, Seoul Health Foundation, Seoul,
Korea
| | - Mihui Kim
- Department of Nursing Science, Jeonju University, Jeonju,
Korea
| | - Mona Choi
- College of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul,
Korea
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16
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Popovic D, Glisic T, Milosavljevic T, Panic N, Marjanovic-Haljilji M, Mijac D, Stojkovic Lalosevic M, Nestorov J, Dragasevic S, Savic P, Filipovic B. The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy. Diagnostics (Basel) 2023; 13:2862. [PMID: 37761229 PMCID: PMC10528171 DOI: 10.3390/diagnostics13182862] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/28/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.
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Affiliation(s)
- Dusan Popovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Tijana Glisic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | | | - Natasa Panic
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Marija Marjanovic-Haljilji
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
| | - Dragana Mijac
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Milica Stojkovic Lalosevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Jelena Nestorov
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Sanja Dragasevic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Gastroenterohepatology, University Clinical Centre of Serbia, 11000 Belgrade, Serbia
| | - Predrag Savic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Clinic for Surgery, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia
| | - Branka Filipovic
- Faculty of Medicine Belgrade, University of Belgrade, 11000 Belgrade, Serbia; (T.G.); (D.M.); (M.S.L.); (J.N.); (S.D.); (P.S.); (B.F.)
- Department of Gastroenterology, Clinical Hospital Center “Dr Dragisa Misovic-Dedinje”, 11000 Belgrade, Serbia; (N.P.); (M.M.-H.)
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17
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Goetz N, Lamba M, Ryan K, Grimpen F. Post-Endoscopy Upper Gastrointestinal Cancer Rate in a Tertiary Referral Centre: An Australian Data Linkage Analysis. J Gastrointest Cancer 2023; 54:837-845. [PMID: 36251210 DOI: 10.1007/s12029-022-00874-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE High-quality gastroscopy is critical for early diagnosis of upper gastrointestinal cancers (UGCs), and assessment of missed cancers may serve as a key quality metric. Using a prospective gastroscopy database and data linkage with the Queensland Cancer Registry, we assessed the risk of developing UGC within 3 years of a cancer-negative gastroscopy at an Australian tertiary centre. Additional aims were to identify factors predictive of missed cancer, perform root cause analyses for missed cancers and assess overall survival. DESIGN/METHOD We identified patients who were diagnosed with UGC within 3 years of undergoing gastroscopy between 2011 and 2016. Non-mucosal cancers, cancers distal to duodenum and patients undergoing surveillance were excluded. Cases diagnosed within 6 months of gastroscopy were defined as detected cancers, while those developing within 6-36 months were defined as missed cancers. Post-endoscopy UGC rate (PEUGIC-3Y) was calculated as ratio of missed over total cancers detected. Demographic, clinical, endoscopic and histologic variables were analysed. RESULTS A total of 17,131 gastroscopies were performed for 10,393 patients during the study period. One hundred and twenty-six UGCs were diagnosed, including 120 detected UGCs and 6 missed UGCs. The overall PEUGIC-3Y rate was 4.8% (95% CI 2.1-10.4). The missed UGCs included 3 gastric adenocarcinomas, 2 gastro-oesophageal junction adenocarcinomas and 1 oesophageal squamous cell carcinoma. At the preceding 'cancer-negative gastroscopy', no macroscopic abnormalities were detected at the site of future UGC in 5/6 patients. A UGC developed in 2/6 patients despite an apparent adequate examination at index gastroscopy. Age, sex, indication for endoscopy and cancer location or histology were not predictive of missed cases, and survival was comparable between groups. CONCLUSION We demonstrate that the PEUGIC-3Y rate was 4.8% (95% CI 2.1-10.4). The majority of missed cases were adenocarcinomas of the gastro-oesophageal junction or stomach and developed in segments which were found to be normal at index gastroscopy, highlighting the challenges in detecting subtle mucosal lesions in the upper gastrointestinal tract. While overall survival between patients with detected and post-gastroscopy cancers was comparable, these ultimately represent potential missed opportunities for diagnosing an early cancer and underscore the need for quality improvement in gastroscopy.
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Affiliation(s)
- Naeman Goetz
- Department of Gastroenterology and Hepatology, Royal Brisbane and Women's Hospital, Herston, 4029, Australia.
| | - Mehul Lamba
- Department of Gastroenterology and Hepatology, Royal Brisbane and Women's Hospital, Herston, 4029, Australia
| | - Kimberley Ryan
- Department of Gastroenterology and Hepatology, Royal Brisbane and Women's Hospital, Herston, 4029, Australia
| | - Florian Grimpen
- Department of Gastroenterology and Hepatology, Royal Brisbane and Women's Hospital, Herston, 4029, Australia
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18
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Fowler GE, Blencowe NS, Hardacre C, Callaway MP, Smart NJ, Macefield R. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of surgical pathology in the abdominopelvic cavity: a systematic review. BMJ Open 2023; 13:e064739. [PMID: 36878659 PMCID: PMC9990659 DOI: 10.1136/bmjopen-2022-064739] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
Abstract
OBJECTIVES There is emerging use of artificial intelligence (AI) models to aid diagnostic imaging. This review examined and critically appraised the application of AI models to identify surgical pathology from radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. DESIGN Systematic review. DATA SOURCES Systematic database searches (Medline, EMBASE, Cochrane Central Register of Controlled Trials) were performed. Date limitations (January 2012 to July 2021) were applied. ELIGIBILITY CRITERIA Primary research studies were considered for eligibility using the PIRT (participants, index test(s), reference standard and target condition) framework. Only publications in the English language were eligible for inclusion in the review. DATA EXTRACTION AND SYNTHESIS Study characteristics, descriptions of AI models and outcomes assessing diagnostic performance were extracted by independent reviewers. A narrative synthesis was performed in accordance with the Synthesis Without Meta-analysis guidelines. Risk of bias was assessed (Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2)). RESULTS Fifteen retrospective studies were included. Studies were diverse in surgical specialty, the intention of the AI applications and the models used. AI training and test sets comprised a median of 130 (range: 5-2440) and 37 (range: 10-1045) patients, respectively. Diagnostic performance of models varied (range: 70%-95% sensitivity, 53%-98% specificity). Only four studies compared the AI model with human performance. Reporting of studies was unstandardised and often lacking in detail. Most studies (n=14) were judged as having overall high risk of bias with concerns regarding applicability. CONCLUSIONS AI application in this field is diverse. Adherence to reporting guidelines is warranted. With finite healthcare resources, future endeavours may benefit from targeting areas where radiological expertise is in high demand to provide greater efficiency in clinical care. Translation to clinical practice and adoption of a multidisciplinary approach should be of high priority. PROSPERO REGISTRATION NUMBER CRD42021237249.
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Affiliation(s)
- George E Fowler
- NIHR Bristol Biomedical Research Centre, Population Health Sciences, Bristol Medical School. University of Bristol, Bristol, UK
| | - Natalie S Blencowe
- NIHR Bristol Biomedical Research Centre, Population Health Sciences, Bristol Medical School. University of Bristol, Bristol, UK
| | - Conor Hardacre
- Bristol Medical School, University of Bristol, Bristol, UK
| | - Mark P Callaway
- Department of Clinical Radiology, University Hospital Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Neil J Smart
- Exeter Surgical Health Services Research Unit (HeSRU), Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Rhiannon Macefield
- NIHR Bristol Biomedical Research Centre, Population Health Sciences, Bristol Medical School. University of Bristol, Bristol, UK
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19
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Wong MW, Rogers BD, Liu MX, Lei WY, Liu TT, Yi CH, Hung JS, Liang SW, Tseng CW, Wang JH, Wu PA, Chen CL. Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD. Diagnostics (Basel) 2023; 13:diagnostics13050960. [PMID: 36900104 PMCID: PMC10000892 DOI: 10.3390/diagnostics13050960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
Novel metrics extracted from pH-impedance monitoring can augment the diagnosis of gastroesophageal reflux disease (GERD). Artificial intelligence (AI) is being widely used to improve the diagnostic capabilities of various diseases. In this review, we update the current literature regarding applications of artificial intelligence in measuring novel pH-impedance metrics. AI demonstrates high performance in the measurement of impedance metrics, including numbers of reflux episodes and post-reflux swallow-induced peristaltic wave index and, furthermore, extracts baseline impedance from the entire pH-impedance study. AI is expected to play a reliable role in facilitating measuring novel impedance metrics in patients with GERD in the near future.
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Affiliation(s)
- Ming-Wun Wong
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
- School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Benjamin D. Rogers
- Division of Gastroenterology, Hepatology and Nutrition, University of Louisville, Louisville, KY 40292, USA
- Division of Gastroenterology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Min-Xiang Liu
- AI Innovation Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Wei-Yi Lei
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Tso-Tsai Liu
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Chih-Hsun Yi
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Jui-Sheng Hung
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Shu-Wei Liang
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
| | - Chiu-Wang Tseng
- NVIDIA AI Technology Center, NVIDIA Corporation, Taipei 11492, Taiwan
| | - Jen-Hung Wang
- Department of Medical Research, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Ping-An Wu
- AI Innovation Research Center, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97004, Taiwan
| | - Chien-Lin Chen
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, 707, Section 3, Chung-Yang Road, Hualien 97004, Taiwan
- Institute of Medical Sciences, Tzu Chi University, Hualien 97004, Taiwan
- Correspondence:
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20
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Li YD, Wang HG, Chen SS, Yu JP, Ruan RW, Jin CH, Chen M, Jin JY, Wang S. Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time. Dig Liver Dis 2023; 55:649-654. [PMID: 36872201 DOI: 10.1016/j.dld.2023.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/09/2023] [Accepted: 02/10/2023] [Indexed: 03/07/2023]
Abstract
BACKGROUND AND AIMS Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learning-based system named Intelligent Detection Endoscopic Assistant-Helicobacter pylori (IDEA-HP) to assess H. pylori infection by using endoscopic videos in real time. METHODS Endoscopic data were retrospectively obtained from Zhejiang Cancer Hospital (ZJCH) for the development, validation, and testing of the system. Stored videos from ZJCH were used for assessing and comparing the performance of IDEA-HP with that of endoscopists. Prospective consecutive patients undergoing esophagogastroduodenoscopy were enrolled to assess the applicability of clinical practice. The urea breath test was used as the gold standard for diagnosing H. pylori infection. RESULTS In 100 videos, IDEA-HP achieved a similar overall accuracy of assessing H. pylori infection to that of experts (84.0% vs. 83.6% [P = 0.729]). Nevertheless, the diagnostic accuracy (84.0% vs. 74.0% [P<0.001]) and sensitivity (82.0% vs. 67.2% [P<0.001]) of IDEA-HP were significantly higher than those of the beginners. In 191 prospective consecutive patients, IDEA-HP achieved accuracy, sensitivity, and specificity of 85.3% (95% CI: 79.0%-89.3%), 83.3% (95% CI: 72.8%-90.5%), and 85.8% (95% CI: 77.7%-91.4%), respectively. CONCLUSIONS Our results show that IDEA-HP has great potential for assisting endoscopists in assessing H. pylori infection status during actual clinical work.
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Affiliation(s)
- Yan-Dong Li
- Department of Endoscopy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Huo-Gen Wang
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Sheng-Sen Chen
- Department of Endoscopy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jiang-Ping Yu
- Department of Endoscopy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Rong-Wei Ruan
- Department of Endoscopy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Chao-Hui Jin
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Ming Chen
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Jia-Yan Jin
- Hithink RoyalFlush Information Network Co., Ltd, Hangzhou, China
| | - Shi Wang
- Department of Endoscopy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
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21
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Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers. Int J Mol Sci 2023; 24:ijms24043316. [PMID: 36834728 PMCID: PMC9968115 DOI: 10.3390/ijms24043316] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/11/2023] Open
Abstract
Esophageal cancer (EC) is the deadliest cancer worldwide, with a 92% annual mortality rate per incidence. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are the two major types of ECs, with EAC having one of the worst prognoses in oncology. Limited screening techniques and a lack of molecular analysis of diseased tissues have led to late-stage presentation and very low survival durations. The five-year survival rate of EC is less than 20%. Thus, early diagnosis of EC may prolong survival and improve clinical outcomes. Cellular and molecular biomarkers are used for diagnosis. At present, esophageal biopsy during upper endoscopy and histopathological analysis is the standard screening modality for both ESCC and EAC. However, this is an invasive method that fails to yield a molecular profile of the diseased compartment. To decrease the invasiveness of the procedures for diagnosis, researchers are proposing non-invasive biomarkers for early diagnosis and point-of-care screening options. Liquid biopsy involves the collection of body fluids (blood, urine, and saliva) non-invasively or with minimal invasiveness. In this review, we have critically discussed various biomarkers and specimen retrieval techniques for ESCC and EAC.
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22
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Katta MR, Kalluru PKR, Bavishi DA, Hameed M, Valisekka SS. Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04625-1. [PMID: 36739356 DOI: 10.1007/s00432-023-04625-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/27/2023] [Indexed: 02/06/2023]
Abstract
PURPOSE This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future. METHODS A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations". RESULTS The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation. CONCLUSION Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.
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Affiliation(s)
| | | | | | - Maha Hameed
- Clinical Research Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
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23
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Meinikheim M, Messmann H, Ebigbo A. Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia. Clin Endosc 2023; 56:14-22. [PMID: 36646423 PMCID: PMC9902686 DOI: 10.5946/ce.2022.247] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 11/25/2022] [Indexed: 01/18/2023] Open
Abstract
Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.
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Affiliation(s)
- Michael Meinikheim
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany,Correspondence: Michael Meinikheim Department of Gastroenterology, University Hospital of Augsburg, Stenglinstr. 2, D-86156 Augsburg, Germany E-mail:
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Alanna Ebigbo
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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24
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Ikenoyama Y, Tanaka K, Umeda Y, Hamada Y, Yukimoto H, Yamada R, Tsuboi J, Nakamura M, Katsurahara M, Horiki N, Nakagawa H. Effect of adding acetic acid when performing magnifying endoscopy with narrow band imaging for diagnosis of Barrett's esophageal adenocarcinoma. Endosc Int Open 2022; 10:E1528-E1536. [PMID: 36531673 PMCID: PMC9754883 DOI: 10.1055/a-1948-2910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/20/2022] [Indexed: 10/14/2022] Open
Abstract
Background and study aims Magnifying endoscopy with narrow band imaging (M-NBI) was developed to diagnose Barrett's esophageal adenocarcinoma (BEA); however, this method remains challenging for inexperienced endoscopists. We aimed to evaluate a modified M-NBI technique that included spraying acetic acid (M-AANBI). Patients and methods Eight endoscopists retrospectively examined 456 endoscopic images obtained from 28 patients with 29 endoscopically resected BEA lesions using three validation schemes: Validation 1 (260 images), wherein the diagnostic performances of M-NBI and M-AANBI were compared - the dataset included 65 images each of BEA and non-neoplastic Barrett's esophagus (NNBE) obtained using each modality; validation 2 (112 images), wherein 56 pairs of M-NBI and M-AANBI images were prepared from the same BEA and NNBE lesions, and diagnoses derived using M-NBI alone were compared to those obtained using both M-NBI and M-AANBI; and validation 3 (84 images), wherein the ease of identifying the BEA demarcation line (DL) was scored via a visual analog scale in 28 patients using magnifying endoscopy with white-light imaging (M-WLI), M-NBI, and M-AANBI. Results For validation 1, M-AANBI was superior to M-NBI in terms of sensitivity (90.8 % vs. 64.6 %), specificity (98.5 % vs. 76.9 %), and accuracy (94.6 % vs. 70.4 %) (all P < 0.05). For validation 2, the accuracy of M-NBI alone was significantly improved when combined with M-AANBI (from 70.5 % to 89.3 %; P < 0.05). For validation 3, M-AANBI had the highest mean score for ease of DL recognition (8.75) compared to M-WLI (3.63) and M-NBI (6.25) (all P < 0.001). Conclusions Using M-AANBI might improve the accuracy of BEA diagnosis.
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Affiliation(s)
- Yohei Ikenoyama
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan,Department of Endoscopy, Mie University Hospital, Tsu, Japan
| | - Kyosuke Tanaka
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan,Department of Endoscopy, Mie University Hospital, Tsu, Japan
| | - Yuhei Umeda
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan,Department of Endoscopy, Mie University Hospital, Tsu, Japan
| | - Yasuhiko Hamada
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Hiroki Yukimoto
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Reiko Yamada
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Junya Tsuboi
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Misaki Nakamura
- Department of Endoscopy, Mie University Hospital, Tsu, Japan
| | | | - Noriyuki Horiki
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and hepatology, Mie University Graduate School of Medicine, Tsu, Japan,Department of Endoscopy, Mie University Hospital, Tsu, Japan
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25
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Islam MM, Poly TN, Walther BA, Yeh CY, Seyed-Abdul S, Li YC(J, Lin MC. Deep Learning for the Diagnosis of Esophageal Cancer in Endoscopic Images: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:cancers14235996. [PMID: 36497480 PMCID: PMC9736434 DOI: 10.3390/cancers14235996] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/17/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Esophageal cancer, one of the most common cancers with a poor prognosis, is the sixth leading cause of cancer-related mortality worldwide. Early and accurate diagnosis of esophageal cancer, thus, plays a vital role in choosing the appropriate treatment plan for patients and increasing their survival rate. However, an accurate diagnosis of esophageal cancer requires substantial expertise and experience. Nowadays, the deep learning (DL) model for the diagnosis of esophageal cancer has shown promising performance. Therefore, we conducted an updated meta-analysis to determine the diagnostic accuracy of the DL model for the diagnosis of esophageal cancer. A search of PubMed, EMBASE, Scopus, and Web of Science, between 1 January 2012 and 1 August 2022, was conducted to identify potential studies evaluating the diagnostic performance of the DL model for esophageal cancer using endoscopic images. The study was performed in accordance with PRISMA guidelines. Two reviewers independently assessed potential studies for inclusion and extracted data from retrieved studies. Methodological quality was assessed by using the QUADAS-2 guidelines. The pooled accuracy, sensitivity, specificity, positive and negative predictive value, and the area under the receiver operating curve (AUROC) were calculated using a random effect model. A total of 28 potential studies involving a total of 703,006 images were included. The pooled accuracy, sensitivity, specificity, and positive and negative predictive value of DL for the diagnosis of esophageal cancer were 92.90%, 93.80%, 91.73%, 93.62%, and 91.97%, respectively. The pooled AUROC of DL for the diagnosis of esophageal cancer was 0.96. Furthermore, there was no publication bias among the studies. The findings of our study show that the DL model has great potential to accurately and quickly diagnose esophageal cancer. However, most studies developed their model using endoscopic data from the Asian population. Therefore, we recommend further validation through studies of other populations as well.
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Affiliation(s)
- Md. Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
| | - Bruno Andreas Walther
- Deep Sea Ecology and Technology, Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Am Handelshafen 12, D-27570 Bremerhaven, Germany
| | - Chih-Yang Yeh
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Seyed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
| | - Yu-Chuan (Jack) Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan
- Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
- Department of Dermatology, Wan Fang Hospital, Taipei 116, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Ming-Chin Lin
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- Department of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan
- Taipei Neuroscience Institute, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence:
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26
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Messmann H, Bisschops R, Antonelli G, Libânio D, Sinonquel P, Abdelrahim M, Ahmad OF, Areia M, Bergman JJGHM, Bhandari P, Boskoski I, Dekker E, Domagk D, Ebigbo A, Eelbode T, Eliakim R, Häfner M, Haidry RJ, Jover R, Kaminski MF, Kuvaev R, Mori Y, Palazzo M, Repici A, Rondonotti E, Rutter MD, Saito Y, Sharma P, Spada C, Spadaccini M, Veitch A, Gralnek IM, Hassan C, Dinis-Ribeiro M. Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy 2022; 54:1211-1231. [PMID: 36270318 DOI: 10.1055/a-1950-5694] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
This ESGE Position Statement defines the expected value of artificial intelligence (AI) for the diagnosis and management of gastrointestinal neoplasia within the framework of the performance measures already defined by ESGE. This is based on the clinical relevance of the expected task and the preliminary evidence regarding artificial intelligence in artificial or clinical settings. MAIN RECOMMENDATIONS:: (1) For acceptance of AI in assessment of completeness of upper GI endoscopy, the adequate level of mucosal inspection with AI should be comparable to that assessed by experienced endoscopists. (2) For acceptance of AI in assessment of completeness of upper GI endoscopy, automated recognition and photodocumentation of relevant anatomical landmarks should be obtained in ≥90% of the procedures. (3) For acceptance of AI in the detection of Barrett's high grade intraepithelial neoplasia or cancer, the AI-assisted detection rate for suspicious lesions for targeted biopsies should be comparable to that of experienced endoscopists with or without advanced imaging techniques. (4) For acceptance of AI in the management of Barrett's neoplasia, AI-assisted selection of lesions amenable to endoscopic resection should be comparable to that of experienced endoscopists. (5) For acceptance of AI in the diagnosis of gastric precancerous conditions, AI-assisted diagnosis of atrophy and intestinal metaplasia should be comparable to that provided by the established biopsy protocol, including the estimation of extent, and consequent allocation to the correct endoscopic surveillance interval. (6) For acceptance of artificial intelligence for automated lesion detection in small-bowel capsule endoscopy (SBCE), the performance of AI-assisted reading should be comparable to that of experienced endoscopists for lesion detection, without increasing but possibly reducing the reading time of the operator. (7) For acceptance of AI in the detection of colorectal polyps, the AI-assisted adenoma detection rate should be comparable to that of experienced endoscopists. (8) For acceptance of AI optical diagnosis (computer-aided diagnosis [CADx]) of diminutive polyps (≤5 mm), AI-assisted characterization should match performance standards for implementing resect-and-discard and diagnose-and-leave strategies. (9) For acceptance of AI in the management of polyps ≥ 6 mm, AI-assisted characterization should be comparable to that of experienced endoscopists in selecting lesions amenable to endoscopic resection.
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Affiliation(s)
- Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Giulio Antonelli
- Gastroenterology and Digestive Endoscopy Unit, Ospedale dei Castelli Hospital, Ariccia, Rome, Italy
- Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Italy
| | - Diogo Libânio
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
- MEDCIDS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pieter Sinonquel
- Department of Gastroenterology and Hepatology, Catholic University of Leuven (KUL), TARGID, University Hospital Leuven, Leuven, Belgium
| | - Mohamed Abdelrahim
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
- Gastrointestinal Services, University College London Hospital, London, UK
| | - Miguel Areia
- Gastroenterology Department, Portuguese Oncology Institute of Coimbra, Coimbra, Portugal
| | | | - Pradeep Bhandari
- Endoscopy Department, Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Ivo Boskoski
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evelien Dekker
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Dirk Domagk
- Department of Medicine I, Josephs-Hospital Warendorf, Academic Teaching Hospital, University of Muenster, Warendorf, Germany
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Tom Eelbode
- Department of Electrical Engineering (ESAT/PSI), Medical Imaging Research Center, KU Leuven, Leuven, Belgium
| | - Rami Eliakim
- Department of Gastroenterology, Sheba Medical Center Tel Hashomer & Sackler School of Medicine, Tel-Aviv University, Ramat Gan, Israel
| | - Michael Häfner
- 2nd Medical Department, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | - Rehan J Haidry
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London Hospital, London, UK
- Division of Surgery and Interventional Sciences, University College London Hospital, London, UK
| | - Rodrigo Jover
- Servicio de Gastroenterología, Hospital General Universitario Dr. Balmis, Instituto de Investigación Biomédica de Alicante ISABIAL, Departamento de Medicina Clínica, Universidad Miguel Hernández, Alicante, Spain
| | - Michal F Kaminski
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Department of Gastroenterology, Hepatology and Clinical Oncology, Centre of Postgraduate Medical Education, Warsaw, Poland
- Department of Oncological Gastroenterology and Department of Cancer Prevention, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Roman Kuvaev
- Endoscopy Department, Yaroslavl Regional Cancer Hospital, Yaroslavl, Russian Federation
- Department of Gastroenterology, Faculty of Additional Professional Education, N.A. Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | | | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | | | - Matthew D Rutter
- North Tees and Hartlepool NHS Foundation Trust, Stockton-on-Tees, UK
- Population Health Sciences Institute, Newcastle University, Newcastle, UK
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Prateek Sharma
- Gastroenterology and Hepatology Division, University of Kansas School of Medicine, Kansas, USA
- Kansas City VA Medical Center, Kansas City, USA
| | - Cristiano Spada
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Digestive Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Andrew Veitch
- Department of Gastroenterology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Center, Afula, Israel
- Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Mario Dinis-Ribeiro
- Department of Gastroenterology, Porto Comprehensive Cancer Center, and RISE@CI-IPOP (Health Research Network), Porto, Portugal
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Park J, Hwang Y, Kim HG, Lee JS, Kim JO, Lee TH, Jeon SR, Hong SJ, Ko BM, Kim S. Reduced detection rate of artificial intelligence in images obtained from untrained endoscope models and improvement using domain adaptation algorithm. Front Med (Lausanne) 2022; 9:1036974. [PMID: 36438041 PMCID: PMC9684642 DOI: 10.3389/fmed.2022.1036974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
A training dataset that is limited to a specific endoscope model can overfit artificial intelligence (AI) to its unique image characteristics. The performance of the AI may degrade in images of different endoscope model. The domain adaptation algorithm, i.e., the cycle-consistent adversarial network (cycleGAN), can transform the image characteristics into AI-friendly styles. We attempted to confirm the performance degradation of AIs in images of various endoscope models and aimed to improve them using cycleGAN transformation. Two AI models were developed from data of esophagogastroduodenoscopies collected retrospectively over 5 years: one for identifying the endoscope models, Olympus CV-260SL, CV-290 (Olympus, Tokyo, Japan), and PENTAX EPK-i (PENTAX Medical, Tokyo, Japan), and the other for recognizing the esophagogastric junction (EGJ). The AIs were trained using 45,683 standardized images from 1,498 cases and validated on 624 separate cases. Between the two endoscope manufacturers, there was a difference in image characteristics that could be distinguished without error by AI. The accuracy of the AI in recognizing gastroesophageal junction was >0.979 in the same endoscope-examined validation dataset as the training dataset. However, they deteriorated in datasets from different endoscopes. Cycle-consistent adversarial network can successfully convert image characteristics to ameliorate the AI performance. The improvements were statistically significant and greater in datasets from different endoscope manufacturers [original → AI-trained style, increased area under the receiver operating characteristic (ROC) curve, P-value: CV-260SL → CV-290, 0.0056, P = 0.0106; CV-260SL → EPK-i, 0.0182, P = 0.0158; CV-290 → CV-260SL, 0.0134, P < 0.0001; CV-290 → EPK-i, 0.0299, P = 0.0001; EPK-i → CV-260SL, 0.0215, P = 0.0024; and EPK-i → CV-290, 0.0616, P < 0.0001]. In conclusion, cycleGAN can transform the diverse image characteristics of endoscope models into an AI-trained style to improve the detection performance of AI.
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Affiliation(s)
- Junseok Park
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Youngbae Hwang
- Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, South Korea
| | - Hyun Gun Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
- *Correspondence: Hyun Gun Kim
| | - Joon Seong Lee
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Jin-Oh Kim
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Tae Hee Lee
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Seong Ran Jeon
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Su Jin Hong
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Bong Min Ko
- Department of Internal Medicine, Soonchunhyang University College of Medicine, Seoul, South Korea
| | - Seokmin Kim
- Department of Intelligent Systems and Robotics, Chungbuk National University, Cheongju, South Korea
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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Tan JL, Chinnaratha MA, Woodman R, Martin R, Chen HT, Carneiro G, Singh R. Diagnostic Accuracy of Artificial Intelligence (AI) to Detect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis. Front Med (Lausanne) 2022; 9:890720. [PMID: 35814747 PMCID: PMC9258946 DOI: 10.3389/fmed.2022.890720] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND AND AIMS Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia. METHODS We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies - 2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostics odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I 2, Tau2 statistics and p-value. The funnel plots and Deek's test were used to assess publication bias. RESULTS Twelve studies comprising of 1,361 patients (utilizing 532,328 images on which the various AI models were trained) were used. The SROC was 0.94 (95% CI: 0.92-0.96). Pooled sensitivity, specificity and diagnostic odds ratio were 90.3% (95% CI: 87.1-92.7%), 84.4% (95% CI: 80.2-87.9%) and 48.1 (95% CI: 28.4-81.5), respectively. Subgroup analysis of AI models trained only on white light endoscopy was similar with pooled sensitivity and specificity of 91.2% (95% CI: 85.7-94.7%) and 85.1% (95% CI: 81.6%-88.1%), respectively. CONCLUSIONS AI is highly accurate at detecting early Barrett's neoplasia and validated for patients with at least high-grade dysplasia and above. Further well-designed prospective randomized controlled studies of all histopathological subtypes of early Barrett's neoplasia are needed to confirm these findings further.
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Affiliation(s)
- Jin Lin Tan
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Mohamed Asif Chinnaratha
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Richard Woodman
- Flinders Centre for Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Rory Martin
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Hsiang-Ting Chen
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwin Hospital, SA Health, Elizabeth Vale, SA, Australia
- Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
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31
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Yang CB, Kim SH, Lim YJ. Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy. Clin Endosc 2022; 55:594-604. [PMID: 35636749 PMCID: PMC9539300 DOI: 10.5946/ce.2021.229] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 03/07/2022] [Indexed: 12/09/2022] Open
Abstract
Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.
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Affiliation(s)
- Chang Bong Yang
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Sang Hoon Kim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea
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Renna F, Martins M, Neto A, Cunha A, Libânio D, Dinis-Ribeiro M, Coimbra M. Artificial Intelligence for Upper Gastrointestinal Endoscopy: A Roadmap from Technology Development to Clinical Practice. Diagnostics (Basel) 2022; 12:1278. [PMID: 35626433 PMCID: PMC9141387 DOI: 10.3390/diagnostics12051278] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/14/2022] [Accepted: 05/18/2022] [Indexed: 02/05/2023] Open
Abstract
Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.
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Affiliation(s)
- Francesco Renna
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal; (M.M.); (A.N.); (A.C.); (M.C.)
- Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
| | - Miguel Martins
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal; (M.M.); (A.N.); (A.C.); (M.C.)
- Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
| | - Alexandre Neto
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal; (M.M.); (A.N.); (A.C.); (M.C.)
- Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - António Cunha
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal; (M.M.); (A.N.); (A.C.); (M.C.)
- Escola de Ciências e Tecnologia, Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
| | - Diogo Libânio
- Departamento de Ciências da Informação e da Decisão em Saúde/Centro de Investigação em Tecnologias e Serviços de Saúde (CIDES/CINTESIS), Faculdade de Medicina, Universidade do Porto, 4200-319 Porto, Portugal; (D.L.); (M.D.-R.)
| | - Mário Dinis-Ribeiro
- Departamento de Ciências da Informação e da Decisão em Saúde/Centro de Investigação em Tecnologias e Serviços de Saúde (CIDES/CINTESIS), Faculdade de Medicina, Universidade do Porto, 4200-319 Porto, Portugal; (D.L.); (M.D.-R.)
| | - Miguel Coimbra
- Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal; (M.M.); (A.N.); (A.C.); (M.C.)
- Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal
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Sun T, Ji C, Li F, Wu H. Hormetic dose responses induced by organic flame retardants in aquatic animals: Occurrence and quantification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 820:153295. [PMID: 35065129 DOI: 10.1016/j.scitotenv.2022.153295] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/15/2022] [Accepted: 01/17/2022] [Indexed: 06/14/2023]
Abstract
The organic flame retardants (OFRs) have attracted global concerns due to their potential toxicity and ubiquitous presence in the aquatic environment. Hormesis refers to a biphasic dose response, characterized by low-dose stimulation and high-dose inhibition. The present study provided substantial evidence for the widespread occurrence of OFRs-induced hormesis in aquatic animals, including 202 hormetic dose response relationships. The maximum stimulatory response (MAX) was commonly lower than 160% of the control response, with a combined value of 134%. Furthermore, the magnitude of MAX varied significantly among multiple factors and their interactions, such as chemical types and taxonomic groups. Moreover, the distance from the dose of MAX to the no-observed-adverse-effect-level (NOAEL) (NOAEL: MAX) was typically below 10-fold (median = 6-fold), while the width of the hormetic zone (from the lowest dose inducing hormesis to the NOAEL) was approximately 20-fold. Collectively, the quantitative features of OFRs-induced hormesis in aquatic animals were in accordance with the broader hormetic literature. In addition, the implications of hormetic dose response model for the risk assessment of OFRs were discussed. This study offered a novel insight for understanding the biological effects of low-to-high doses of OFRs on aquatic animals and assessing the potential risks of OFRs in the aquatic environment.
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Affiliation(s)
- Tao Sun
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China.
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Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y. Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc 2022; 95:599-609.e7. [PMID: 34979114 DOI: 10.1016/j.gie.2021.12.021] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists. METHODS PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis. RESULTS Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively. CONCLUSIONS The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.
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Affiliation(s)
- Fang Xie
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Keqiang Zhang
- Second Hospital of Jilin University, Changchun, Jilin, China
| | - Feng Li
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Guorong Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Ni
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Wei Zhang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Junchao Wang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, Jilin, China
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Visaggi P, Barberio B, Gregori D, Azzolina D, Martinato M, Hassan C, Sharma P, Savarino E, de Bortoli N. Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases. Aliment Pharmacol Ther 2022; 55:528-540. [PMID: 35098562 PMCID: PMC9305819 DOI: 10.1111/apt.16778] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/09/2022] [Accepted: 01/09/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs). AIM We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD. METHODS We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated. RESULTS For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively. CONCLUSIONS AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
| | - Brigida Barberio
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Dario Gregori
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Danila Azzolina
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
- Department of Medical ScienceUniversity of FerraraFerraraItaly
| | - Matteo Martinato
- Unit of Biostatistics, Epidemiology and Public HealthDepartment of Cardiac, Thoracic, Vascular Sciences and Public HealthUniversity of PadovaPadovaItaly
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas UniversityVia Rita Levi Montalcini 420072 Pieve Emanuele, MilanItaly
- IRCCS Humanitas Research Hospitalvia Manzoni 5620089 Rozzano, MilanItaly
| | - Prateek Sharma
- University of Kansas School of Medicine and VA Medical CenterKansas CityMissouriUSA
| | - Edoardo Savarino
- Division of GastroenterologyDepartment of Surgery, Oncology and GastroenterologyUniversity of PadovaPadovaItaly
| | - Nicola de Bortoli
- Gastroenterology UnitDepartment of Translational Research and New Technologies in Medicine and SurgeryUniversity of PisaPisaItaly
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Nisha J, P. Gopi V, Palanisamy P. Automated colorectal polyp detection based on image enhancement and dual-path CNN architecture. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Zhao Y, Hu B, Wang Y, Yin X, Jiang Y, Zhu X. Identification of gastric cancer with convolutional neural networks: a systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:11717-11736. [PMID: 35221775 PMCID: PMC8856868 DOI: 10.1007/s11042-022-12258-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/20/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 - 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.
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Affiliation(s)
- Yuxue Zhao
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Bo Hu
- Department of Thoracic Surgery, Qingdao Municipal Hospital, Qingdao, China
| | - Ying Wang
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Xiaomeng Yin
- Pediatrics Intensive Care Unit, Qingdao Municipal Hospital, Qingdao, China
| | - Yuanyuan Jiang
- International Medical Services, Qilu Hospital of Shandong University, Jinan, China
| | - Xiuli Zhu
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
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Dutta AK. Are we Missing Barrett's Esophagus in Our Busy Endoscopy Practice? Improving Detection. JOURNAL OF DIGESTIVE ENDOSCOPY 2022. [DOI: 10.1055/s-0041-1741465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
AbstractBarrett's esophagus (BE) denotes the replacement of stratified squamous epithelium of esophagus by columnar epithelium. It is associated with a significantly increased risk of esophageal adenocarcinoma and hence patients with BE are advised endoscopic surveillance for early detection of dysplastic and neoplastic lesions. Esophageal cancer is the sixth most common cancer in terms of incidence and mortality in India. Around 15 to 25% of esophageal cancers are adenocarcinoma. BE is likely to be an important precursor of esophageal adenocarcinoma and we may be missing patients with BE in our busy endoscopy practice. The detection of BE may be improved by identifying high-risk groups, performing thorough endoscopic examination, and applying newer imaging techniques. The high-risk group includes patients with chronic gastroesophageal reflux disease, obesity, smoking, etc. During endoscopic examination, a careful assessment of the gastroesophageal junction and identification of important landmarks such as gastroesophageal junction and Z line are essential to detect BE. Management of BE depends on the detection of dysplasia and for this four quadrant mucosal biopsy is recommended every 1 to 2 cm. However, random biopsy samples only a small area of mucosa and advanced technologies for real-time detection of dysplasia and neoplasia may overcome this limitation. In this review, we discuss the current scenario of BE in India and ways to improve the detection of BE including dysplastic lesions.
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Affiliation(s)
- Amit Kumar Dutta
- Department of Gastrointestinal Sciences, Christian Medical College and Hospital, Vellore, Tamil Nadu, India
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Jayakumar S, Sounderajah V, Normahani P, Harling L, Markar SR, Ashrafian H, Darzi A. Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. NPJ Digit Med 2022; 5:11. [PMID: 35087178 PMCID: PMC8795185 DOI: 10.1038/s41746-021-00544-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 11/28/2021] [Indexed: 01/05/2023] Open
Abstract
Artificial intelligence (AI) centred diagnostic systems are increasingly recognised as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed the quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. Two hundred forty-three of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates the incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI-specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.
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Affiliation(s)
- Shruti Jayakumar
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Viknesh Sounderajah
- Department of Surgery and Cancer, Imperial College London, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Pasha Normahani
- Department of Surgery and Cancer, Imperial College London, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Leanne Harling
- Department of Surgery and Cancer, Imperial College London, London, UK
- Department of Thoracic Surgery, Guy's Hospital, London, UK
| | - Sheraz R Markar
- Department of Surgery and Cancer, Imperial College London, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Hutan Ashrafian
- Department of Surgery and Cancer, Imperial College London, London, UK.
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, UK
- Institute of Global Health Innovation, Imperial College London, London, UK
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Liu W, Yuan X, Guo L, Pan F, Wu C, Sun Z, Tian F, Yuan C, Zhang W, Bai S, Feng J, Hu Y, Hu B. Artificial Intelligence for Detecting and Delineating Margins of Early ESCC Under WLI Endoscopy. Clin Transl Gastroenterol 2022; 13:e00433. [PMID: 35130184 PMCID: PMC8806389 DOI: 10.14309/ctg.0000000000000433] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 10/13/2021] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Conventional white light imaging (WLI) endoscopy is the most common screening technique used for detecting early esophageal squamous cell carcinoma (ESCC). Nevertheless, it is difficult to detect and delineate margins of early ESCC using WLI endoscopy. This study aimed to develop an artificial intelligence (AI) model to detect and delineate margins of early ESCC under WLI endoscopy. METHODS A total of 13,083 WLI images from 1,239 patients were used to train and test the AI model. To evaluate the detection performance of the model, 1,479 images and 563 images were used as internal and external validation data sets, respectively. For assessing the delineation performance of the model, 1,114 images and 211 images were used as internal and external validation data sets, respectively. In addition, 216 images were used to compare the delineation performance between the model and endoscopists. RESULTS The model showed an accuracy of 85.7% and 84.5% in detecting lesions in internal and external validation, respectively. For delineating margins, the model achieved an accuracy of 93.4% and 95.7% in the internal and external validation, respectively, under an overlap ratio of 0.60. The accuracy of the model, senior endoscopists, and expert endoscopists in delineating margins were 98.1%, 78.6%, and 95.3%, respectively. The proposed model achieved similar delineating performance compared with that of expert endoscopists but superior to senior endoscopists. DISCUSSION We successfully developed an AI model, which can be used to accurately detect early ESCC and delineate the margins of the lesions under WLI endoscopy.
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Affiliation(s)
- Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Linjie Guo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Feng Pan
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, Jiangsu, China;
| | - Chuncheng Wu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhongshang Sun
- Department of Gastroenterology, Huai'an First People's Hospital, Huai'an, Jiangsu, China;
| | - Feng Tian
- Department of Gastroenterology, Zigong Fourth People's Hospital, Zigong, Sichuan, China;
| | - Cong Yuan
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China;
| | - Wanhong Zhang
- Department of Gastroenterology, Cangxi People's Hospital, Guangyuan, Sichuan, China;
| | - Shuai Bai
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Feng
- Xiamen Innovision Medical Technology Co, Ltd, Xiamen, Fujian, China.
| | - Yanxing Hu
- Xiamen Innovision Medical Technology Co, Ltd, Xiamen, Fujian, China.
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2:124-132. [DOI: 10.35712/aig.v2.i5.124] [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: 06/06/2021] [Revised: 06/26/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
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Hamade N, Sharma P. 'Artificial intelligence in Barrett's Esophagus'. Ther Adv Gastrointest Endosc 2021; 14:26317745211049964. [PMID: 34671724 PMCID: PMC8521738 DOI: 10.1177/26317745211049964] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 09/07/2021] [Indexed: 12/26/2022] Open
Abstract
Despite advances in endoscopic imaging modalities, there are still significant miss rates of dysplasia and cancer in Barrett's esophagus. Artificial intelligence (AI) is a promising tool that may potentially be a useful adjunct to the endoscopist in detecting subtle dysplasia and cancer. Studies have shown AI systems have a sensitivity of more than 90% and specificity of more than 80% in detecting Barrett's related dysplasia and cancer. Beyond visual detection and diagnosis, AI may also prove to be useful in quality control, streamlining clinical work, documentation, and lessening the administrative load on physicians. Research in this area is advancing at a rapid rate, and as the field expands, regulations and guidelines will need to be put into place to better regulate the growth and use of AI. This review provides an overview of the present and future role of AI in Barrett's esophagus.
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Affiliation(s)
- Nour Hamade
- Department of Gastroenterology and Hepatology, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, Veteran Affairs Medical Center, 4801 E. Linwood Boulevard, Kansas City, MO 6412, USA
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Fowler GE, Macefield RC, Hardacre C, Callaway MP, Smart NJ, Blencowe NS. Artificial intelligence as a diagnostic aid in cross-sectional radiological imaging of the abdominopelvic cavity: a protocol for a systematic review. BMJ Open 2021; 11:e054411. [PMID: 34670769 PMCID: PMC8529972 DOI: 10.1136/bmjopen-2021-054411] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION The application of artificial intelligence (AI) technologies as a diagnostic aid in healthcare is increasing. Benefits include applications to improve health systems, such as rapid and accurate interpretation of medical images. This may improve the performance of diagnostic, prognostic and management decisions. While a large amount of work has been undertaken discussing the role of AI little is understood regarding the performance of such applications in the clinical setting. This systematic review aims to critically appraise the diagnostic performance of AI algorithms to identify disease from cross-sectional radiological images of the abdominopelvic cavity, to identify current limitations and inform future research. METHODS AND ANALYSIS A systematic search will be conducted on Medline, EMBASE and the Cochrane Central Register of Controlled Trials to identify relevant studies. Primary studies where AI-based technologies have been used as a diagnostic aid in cross-sectional radiological images of the abdominopelvic cavity will be included. Diagnostic accuracy of AI models, including reported sensitivity, specificity, predictive values, likelihood ratios and the area under the receiver operating characteristic curve will be examined and compared with standard practice. Risk of bias of included studies will be assessed using the QUADAS-2 tool. Findings will be reported according to the Synthesis Without Meta-analysis guidelines. ETHICS AND DISSEMINATION No ethical approval is required as primary data will not be collected. The results will inform further research studies in this field. Findings will be disseminated at relevant conferences, on social media and published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER CRD42021237249.
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Affiliation(s)
- George E Fowler
- Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Rhiannon C Macefield
- Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Conor Hardacre
- Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mark P Callaway
- Department of Clinical Radiology, Bristol Royal Infirmary, Bristol, UK
| | - Neil J Smart
- Exeter Surgical Health Services Research Unit (HeSRU), Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Natalie S Blencowe
- Centre for Surgical Research, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021; 31:743-758. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [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] [Indexed: 02/04/2023]
Abstract
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA
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Abstract
PURPOSE OF REVIEW Artificial intelligence is becoming rapidly integrated into modern technology including medicine. Artificial intelligence has a wide range of potential in gastroenterology, particularly with endoscopy, given the required analysis of large datasets of images. The aim of this review is to summarize the advances of artificial intelligence in gastroenterology (GI) endoscopy over the past year. RECENT FINDINGS Computer-aided detection (CADe) systems during real-time colonoscopy have resulted in increased adenoma detection rate with no significant increase in procedure times. Deep learning techniques have been utilized to accurately assess bowel preparation quality, which would impact surveillance colonoscopy recommendations. For the upper GI tract, CADe systems have been developed to aid in improving the diagnosis of Barrett's neoplasia during real-time endoscopy. Artificial intelligence-assisted real-time endoscopy has been shown to reduce blind spots during EGD. SUMMARY The application of artificial intelligence in gastroenterology endoscopy remains promising. Advances over the past year include improved detection of GI neoplasia during endoscopy and characterization of lesions. Further research including randomized, multicenter trials are needed to further evaluate the use of artificial intelligence for real-time endoscopy.
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Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021; 11:diagnostics11091575. [PMID: 34573917 PMCID: PMC8469485 DOI: 10.3390/diagnostics11091575] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/16/2022] Open
Abstract
The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.
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Affiliation(s)
- Silvia Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Center for Endoscopic Research Therapeutics and Training (CERTT), Catholic University, 00168 Rome, Italy
- Correspondence: (S.P.); (L.H.E.)
| | - Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Fondazione Poliambulanza Istituto Ospedaliero, 25121 Brescia, Italy
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Andrea Telese
- Department of Gastroenterology, University College London Hospital (UCLH), London NW1 2AF, UK;
| | - Leonardo Henry Eusebi
- Division of Gastroenterology and Endoscopy, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40121 Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, 40121 Bologna, Italy
- Correspondence: (S.P.); (L.H.E.)
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Correia FP, Lourenço LC. Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina? World J Gastroenterol 2021; 27:5351-5361. [PMID: 34539137 PMCID: PMC8409168 DOI: 10.3748/wjg.v27.i32.5351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 05/15/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
Abstract
The close relationship of medicine with technology and the particular interest in this symbiosis in recent years has led to the development of several computed artificial intelligence (AI) systems aimed at various areas of medicine. A number of studies have demonstrated that those systems allow accurate diagnoses with histological precision, thus facilitating decision-making by clinicians in real time. In the field of gastroenterology, AI has been applied in the diagnosis of pathologies of the entire digestive tract and their attached glands, and are increasingly accepted for the detection of colorectal polyps and confirming their histological classification. Studies have shown high accuracy, sensitivity, and specificity in relation to expert endoscopists, and mainly in relation to those with less experience. Other applications that are increasingly studied and with very promising results are the investigation of dysplasia in patients with Barrett's esophagus and the endoscopic and histological assessment of colon inflammation in patients with ulcerative colitis. In some cases AI is thus better than or at least equal to human abilities. However, additional studies are needed to reinforce the existing data, and mainly to determine the applicability of this technology in other indications. This review summarizes the state of the art of AI in gastroenterological pathology.
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Affiliation(s)
- Fábio Pereira Correia
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
| | - Luís Carvalho Lourenço
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
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Ahmad OF. Early detection of gastric neoplasia: is artificial intelligence the solution? Lancet Gastroenterol Hepatol 2021; 6:678-679. [PMID: 34297943 DOI: 10.1016/s2468-1253(21)00254-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/25/2021] [Indexed: 11/30/2022]
Affiliation(s)
- Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London W1W 7TS, UK.
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