Pub Date : 2024-06-08DOI: 10.37126/aige.v5.i2.90704
Ayrton I Bangolo, Nikita Wadhwani, V. Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Plumptre, Imranjot Sekhon, Abraham Lo, Simcha I Weissman
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
{"title":"Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies","authors":"Ayrton I Bangolo, Nikita Wadhwani, V. Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Plumptre, Imranjot Sekhon, Abraham Lo, Simcha I Weissman","doi":"10.37126/aige.v5.i2.90704","DOIUrl":"https://doi.org/10.37126/aige.v5.i2.90704","url":null,"abstract":"The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.","PeriodicalId":495606,"journal":{"name":"Artificial intelligence in gastrointestinal endoscopy","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.
{"title":"Role of artificial intelligence in colorectal cancer","authors":"Gita Lingam, Taner Shakir, Rawen Kader, Manish Chand","doi":"10.37126/aige.v5.i2.90723","DOIUrl":"https://doi.org/10.37126/aige.v5.i2.90723","url":null,"abstract":"The sphere of artificial intelligence (AI) is ever expanding. Applications for clinical practice have been emerging over recent years. Although its uptake has been most prominent in endoscopy, this represents only one aspect of holistic patient care. There are a multitude of other potential avenues in which gastrointestinal care may be involved. We aim to review the role of AI in colorectal cancer as a whole. We performed broad scoping and focused searches of the applications of AI in the field of colorectal cancer. All trials including qualitative research were included from the year 2000 onwards. Studies were grouped into pre-operative, intra-operative and post-operative aspects. Pre-operatively, the major use is with endoscopic recognition. Colonoscopy has embraced the use for human derived classifications such as Narrow-band Imaging International Colorectal Endoscopic, Japan Narrow-band Imaging Expert Team, Paris and Kudo. However, novel detection and diagnostic methods have arisen from advances in AI classification. Intra-operatively, adjuncts such as image enhanced identification of structures and assessment of perfusion have led to improvements in clinical outcomes. Post-operatively, monitoring and surveillance have taken strides with potential socioeconomic and environmental savings. The uses of AI within the umbrella of colorectal surgery are multiple. We have identified existing technologies which are already augmenting cancer care. The future applications are exciting and could at least match, if not surpass human standards.","PeriodicalId":495606,"journal":{"name":"Artificial intelligence in gastrointestinal endoscopy","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-08DOI: 10.37126/aige.v5.i2.92090
Tuấn Quang Dương, Jonathan Soldera
BACKGROUND Virtual reality (VR) has emerged as an innovative technology in endoscopy training, providing a simulated environment that closely resembles real-life scenarios and offering trainees a valuable platform to acquire and enhance their endoscopic skills. This systematic review will critically evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. AIM To evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. By examining the current state of the field, this review seeks to identify gaps, challenges, and opportunities for further research and implementation of VR in endoscopic training. METHODS The study is a systematic review, following the guidelines for reporting systematic reviews set out by the PRISMA statement. A comprehensive search command was designed and implemented and run in September 2023 to identify relevant studies available, from electronic databases such as PubMed, Scopus, Cochrane, and Google Scholar. The results were systematically reviewed. RESULTS Sixteen articles were included in the final analysis. The total number of participants was 523. Five studies focused on both upper endoscopy and colonoscopy training, two on upper endoscopy training only, eight on colonoscopy training only, and one on sigmoidoscopy training only. Gastrointestinal Mentor virtual endoscopy simulator was commonly used. Fifteen reported positive results, indicating that VR-based training was feasible and acceptable for endoscopy learners. VR technology helped the trainees enhance their skills in manipulating the endoscope, reducing the procedure time or increasing the technical accuracy, in VR scenarios and real patients. Some studies show that the patient discomfort level decreased significantly. However, some studies show there were no significant differences in patient discomfort and pain scores between VR group and other groups. CONCLUSION VR training is effective for endoscopy training. There are several well-designed randomized controlled trials with large sample sizes, proving the potential of this innovative tool. Thus, VR should be more widely adopted in endoscopy training. Furthermore, combining VR training with conventional methods could be a promising approach that should be implemented in training.
{"title":"Virtual reality tools for training in gastrointestinal endoscopy: A systematic review","authors":"Tuấn Quang Dương, Jonathan Soldera","doi":"10.37126/aige.v5.i2.92090","DOIUrl":"https://doi.org/10.37126/aige.v5.i2.92090","url":null,"abstract":"BACKGROUND\u0000 Virtual reality (VR) has emerged as an innovative technology in endoscopy training, providing a simulated environment that closely resembles real-life scenarios and offering trainees a valuable platform to acquire and enhance their endoscopic skills. This systematic review will critically evaluate the effectiveness and feasibility of VR-based training compared to traditional methods.\u0000 AIM\u0000 To evaluate the effectiveness and feasibility of VR-based training compared to traditional methods. By examining the current state of the field, this review seeks to identify gaps, challenges, and opportunities for further research and implementation of VR in endoscopic training.\u0000 METHODS\u0000 The study is a systematic review, following the guidelines for reporting systematic reviews set out by the PRISMA statement. A comprehensive search command was designed and implemented and run in September 2023 to identify relevant studies available, from electronic databases such as PubMed, Scopus, Cochrane, and Google Scholar. The results were systematically reviewed.\u0000 RESULTS\u0000 Sixteen articles were included in the final analysis. The total number of participants was 523. Five studies focused on both upper endoscopy and colonoscopy training, two on upper endoscopy training only, eight on colonoscopy training only, and one on sigmoidoscopy training only. Gastrointestinal Mentor virtual endoscopy simulator was commonly used. Fifteen reported positive results, indicating that VR-based training was feasible and acceptable for endoscopy learners. VR technology helped the trainees enhance their skills in manipulating the endoscope, reducing the procedure time or increasing the technical accuracy, in VR scenarios and real patients. Some studies show that the patient discomfort level decreased significantly. However, some studies show there were no significant differences in patient discomfort and pain scores between VR group and other groups.\u0000 CONCLUSION\u0000 VR training is effective for endoscopy training. There are several well-designed randomized controlled trials with large sample sizes, proving the potential of this innovative tool. Thus, VR should be more widely adopted in endoscopy training. Furthermore, combining VR training with conventional methods could be a promising approach that should be implemented in training.","PeriodicalId":495606,"journal":{"name":"Artificial intelligence in gastrointestinal endoscopy","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-08DOI: 10.37126/aige.v5.i2.91424
N. Ghosh, Ashok Kumar
Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc. ) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.
{"title":"Ultra-minimally invasive endoscopic techniques and colorectal diseases: Current status and its future","authors":"N. Ghosh, Ashok Kumar","doi":"10.37126/aige.v5.i2.91424","DOIUrl":"https://doi.org/10.37126/aige.v5.i2.91424","url":null,"abstract":"Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc. ) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.","PeriodicalId":495606,"journal":{"name":"Artificial intelligence in gastrointestinal endoscopy","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.37126/aige.v5.i1.90574
Q. V. D. van der Zander, R. Schreuder, A. Thijssen, C. H. J. Kusters, N. Dehghani, T. Scheeve, Bjorn Winkens, Mirjam C. M. van der Ende - van Loon, P. D. de With, F. van der Sommen, Ad A M Masclee, E. Schoon
BACKGROUND Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps. AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated. METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations. RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist ). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best. CONCLUSION Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.
{"title":"Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems","authors":"Q. V. D. van der Zander, R. Schreuder, A. Thijssen, C. H. J. Kusters, N. Dehghani, T. Scheeve, Bjorn Winkens, Mirjam C. M. van der Ende - van Loon, P. D. de With, F. van der Sommen, Ad A M Masclee, E. Schoon","doi":"10.37126/aige.v5.i1.90574","DOIUrl":"https://doi.org/10.37126/aige.v5.i1.90574","url":null,"abstract":"BACKGROUND\u0000 Artificial intelligence (AI) has potential in the optical diagnosis of colorectal polyps.\u0000 AIM\u0000 To evaluate the feasibility of the real-time use of the computer-aided diagnosis system (CADx) AI for ColoRectal Polyps (AI4CRP) for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYETM (Fujifilm, Tokyo, Japan). CADx influence on the optical diagnosis of an expert endoscopist was also investigated.\u0000 METHODS\u0000 AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm. Both CADx-systems exploit convolutional neural networks. Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard. AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value (range 0.0-1.0). A predefined cut-off value of 0.6 was set with values < 0.6 indicating benign and values ≥ 0.6 indicating premalignant colorectal polyps. Low confidence characterizations were defined as values 40% around the cut-off value of 0.6 (< 0.36 and > 0.76). Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.\u0000 RESULTS\u0000 AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps. Self-critical AI4CRP, excluding 14 low confidence characterizations [27.5% (14/51)], had a diagnostic accuracy of 89.2%, sensitivity of 89.7%, and specificity of 87.5%, which was higher compared to AI4CRP. CAD EYE had a 83.7% diagnostic accuracy, 74.2% sensitivity, and 100.0% specificity. Diagnostic performances of the endoscopist alone (before AI) increased non-significantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE (AI-assisted endoscopist ). Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems, except for specificity for which CAD EYE performed best.\u0000 CONCLUSION\u0000 Real-time use of AI4CRP was feasible. Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.","PeriodicalId":495606,"journal":{"name":"Artificial intelligence in gastrointestinal endoscopy","volume":"48 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140076942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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