151
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Clinical Evaluation of AI in Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_310-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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152
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Sumiyama K, Futakuchi T, Kamba S, Matsui H, Tamai N. Artificial intelligence in endoscopy: Present and future perspectives. Dig Endosc 2021; 33:218-230. [PMID: 32935376 DOI: 10.1111/den.13837] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/04/2020] [Indexed: 02/08/2023]
Abstract
Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL-based models to real-time computer-aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL-based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles.
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Affiliation(s)
- Kazuki Sumiyama
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Toshiki Futakuchi
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Shunsuke Kamba
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Hiroaki Matsui
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
| | - Naoto Tamai
- Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan
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153
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Misawa M, Kudo SE, Mori Y, Maeda Y, Ogawa Y, Ichimasa K, Kudo T, Wakamura K, Hayashi T, Miyachi H, Baba T, Ishida F, Itoh H, Oda M, Mori K. Current status and future perspective on artificial intelligence for lower endoscopy. Dig Endosc 2021; 33:273-284. [PMID: 32969051 DOI: 10.1111/den.13847] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/03/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022]
Abstract
The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Heath and Society, University of Oslo, Oslo, Norway
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Aichi, Japan
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154
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Leung FW, Hsieh YH. Artificial intelligence (computer-assisted detection) is the most recent novel approach to increase adenoma detection. Gastrointest Endosc 2021; 93:86-88. [PMID: 33353642 DOI: 10.1016/j.gie.2020.07.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 07/28/2020] [Indexed: 02/08/2023]
Affiliation(s)
- Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, California, USA; David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California, USA
| | - Yu-Hsi Hsieh
- Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Dalin, Chiayi, Taiwan; Tzu Chi University, Hualien City, Hualien, Taiwan
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155
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Ang TL, Carneiro G. Artificial intelligence in gastrointestinal endoscopy. J Gastroenterol Hepatol 2021; 36:5-6. [PMID: 33448513 DOI: 10.1111/jgh.15344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 12/20/2022]
Affiliation(s)
- Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore.,Medicine Academic Clinical Programme, SingHealth Duke-NUS Academic Medical Centre, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
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156
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Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc 2021; 93:77-85.e6. [PMID: 32598963 DOI: 10.1016/j.gie.2020.06.059] [Citation(s) in RCA: 255] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND AIMS One-fourth of colorectal neoplasia are missed at screening colonoscopy, representing the main cause of interval colorectal cancer. Deep learning systems with real-time computer-aided polyp detection (CADe) showed high accuracy in artificial settings, and preliminary randomized controlled trials (RCTs) reported favorable outcomes in the clinical setting. The aim of this meta-analysis was to summarize available RCTs on the performance of CADe systems in colorectal neoplasia detection. METHODS We searched MEDLINE, EMBASE, and Cochrane Central databases until March 2020 for RCTs reporting diagnostic accuracy of CADe systems in the detection of colorectal neoplasia. The primary outcome was pooled adenoma detection rate (ADR), and secondary outcomes were adenoma per colonoscopy (APC) according to size, morphology, and location; advanced APC; polyp detection rate; polyps per colonoscopy; and sessile serrated lesions per colonoscopy. We calculated risk ratios (RRs), performed subgroup and sensitivity analyses, and assessed heterogeneity and publication bias. RESULTS Overall, 5 randomized controlled trials (4354 patients) were included in the final analysis. Pooled ADR was significantly higher in the CADe group than in the control group (791/2163 [36.6%] vs 558/2191 [25.2%]; RR, 1.44; 95% confidence interval [CI], 1.27-1.62; P < .01; I2 = 42%). APC was also higher in the CADe group compared with control (1249/2163 [.58] vs 779/2191 [.36]; RR, 1.70; 95% CI, 1.53-1.89; P < .01; I2 = 33%). APC was higher for ≤5-mm (RR, 1.69; 95% CI, 1.48-1.84), 6- to 9-mm (RR, 1.44; 95% CI, 1.19-1.75), and ≥10-mm adenomas (RR, 1.46; 95% CI, 1.04-2.06) and for proximal (RR, 1.59; 95% CI, 1.34-1.88), distal (RR, 1.68; 95% CI, 1.50-1.88), flat (RR, 1.78; 95% CI, 1.47-2.15), and polypoid morphology (RR, 1.54; 95% CI, 1.40-1.68). Regarding histology, CADe resulted in a higher sessile serrated lesion per colonoscopy (RR, 1.52; 95% CI, 1.14-2.02), whereas a nonsignificant trend for advanced ADR was found (RR, 1.35; 95% CI, .74-2.47; P = .33; I2 = 69%). Level of evidence for RCTs was graded as moderate. CONCLUSIONS According to available evidence, the incorporation of artificial intelligence as aid for detection of colorectal neoplasia results in a significant increase in the detection of colorectal neoplasia, and such effect is independent from main adenoma characteristics.
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157
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Lin AL, Chen WC, Hong JC. Electronic health record data mining for artificial intelligence healthcare. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00008-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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158
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Mori Y, Neumann H, Misawa M, Kudo SE, Bretthauer M. Artificial intelligence in colonoscopy - Now on the market. What's next? J Gastroenterol Hepatol 2021; 36:7-11. [PMID: 33179322 DOI: 10.1111/jgh.15339] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 11/05/2020] [Indexed: 12/15/2022]
Abstract
Adoption of artificial intelligence (AI) in clinical medicine is revolutionizing daily practice. In the field of colonoscopy, major endoscopy manufacturers have already launched their own AI products on the market with regulatory approval in Europe and Asia. This commercialization is strongly supported by positive evidence that has been recently established through rigorously designed prospective trials and randomized controlled trials. According to some of the trials, AI tools possibly increase the adenoma detection rate by roughly 50% and contribute to a 7-20% reduction of colonoscopy-related costs. Given that reliable evidence is emerging, together with active commercialization, this seems to be a good time for us to review and discuss the current status of AI in colonoscopy from a clinical perspective. In this review, we introduce the advantages and possible drawbacks of AI tools and explore their future potential including the possibility of obtaining reimbursement.
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Affiliation(s)
- Yuichi Mori
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Helmut Neumann
- Interdisciplinary Endoscopy Center, University Medical Center Mainz, Mainz, Germany
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Michael Bretthauer
- Clinical Effectiveness Research Group, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
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159
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Sinonquel P, Bisschops R. Striving for quality improvement: can artificial intelligence help? Best Pract Res Clin Gastroenterol 2020; 52-53:101722. [PMID: 34172249 DOI: 10.1016/j.bpg.2020.101722] [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: 12/10/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.
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Affiliation(s)
- P Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - R Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
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160
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Development and Validation of an Automatic Image-Recognition Endoscopic Report Generation System: A Multicenter Study. Clin Transl Gastroenterol 2020; 12:e00282. [PMID: 33395075 PMCID: PMC7771723 DOI: 10.14309/ctg.0000000000000282] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 11/05/2020] [Indexed: 12/18/2022] Open
Abstract
INTRODUCTION: Conventional gastrointestinal (GI) endoscopy reports written by physicians are time consuming and might have obvious heterogeneity or omissions, impairing the efficiency and multicenter consultation potential. We aimed to develop and validate an image recognition–based structured report generation system (ISRGS) through a multicenter database and to assess its diagnostic performance. Methods: First, we developed and evaluated an ISRGS combining real-time video capture, site identification, lesion detection, subcharacteristics analysis, and structured report generation. White light and chromoendoscopy images from patients with GI lesions were eligible for study inclusion. A total of 46,987 images from 9 tertiary hospitals were used to train, validate, and multicenter test (6:2:2). Moreover, 5,699 images were prospectively enrolled from Qilu Hospital of Shandong University to further assess the system in a prospective test set. The primary outcome was the diagnosis performance of GI lesions in multicenter and prospective tests. Results: The overall accuracy in identifying early esophageal cancer, early gastric cancer, early colorectal cancer, esophageal varices, reflux esophagitis, Barrett’s esophagus, chronic atrophic gastritis, gastric ulcer, colorectal polyp, and ulcerative colitis was 0.8841 (95% confidence interval, 0.8775–0.8904) and 0.8965 (0.8883–0.9041) in multicenter and prospective tests, respectively. The accuracy of cecum and upper GI site identification were 0.9978 (0.9969–0.9984) and 0.8513 (0.8399–0.8620), respectively. The accuracy of staining discrimination was 0.9489 (0.9396–0.9568). The relative error of size measurement was 4.04% (range 0.75%–7.39%). DISCUSSION: ISRGS is a reliable computer-aided endoscopic report generation system that might assist endoscopists working at various hospital levels to generate standardized and accurate endoscopy reports (http://links.lww.com/CTG/A485).
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161
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Antonelli G, Gkolfakis P, Tziatzios G, Papanikolaou IS, Triantafyllou K, Hassan C. Artificial intelligence-aided colonoscopy: Recent developments and future perspectives. World J Gastroenterol 2020; 26:7436-7443. [PMID: 33384546 PMCID: PMC7754556 DOI: 10.3748/wjg.v26.i47.7436] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/18/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) systems, especially after the successful application of Convolutional Neural Networks, are revolutionizing modern medicine. Gastrointestinal Endoscopy has shown to be a fertile terrain for the development of AI systems aiming to aid endoscopists in various aspects of their daily activity. Lesion detection can be one of the two main aspects in which AI can increase diagnostic yield and abilities of endoscopists. In colonoscopy, it is well known that a substantial rate of missed neoplasia is still present, representing the major cause of interval cancer. In addition, an extremely high variability in adenoma detection rate, the main key quality indicator in colonoscopy, has been extensively reported. The other domain in which AI is believed to have a considerable impact on everyday clinical practice is lesion characterization and aid in “optical diagnosis”. By predicting in vivo histology, such pathology costs may be averted by the implementation of two separate but synergistic strategies, namely the “leave-in-situ” strategy for < 5 mm hyperplastic lesions in the rectosigmoid tract, and “resect and discard” for the other diminutive lesions. In this opinion review we present current available evidence regarding the role of AI in improving lesions’ detection and characterization during colonoscopy.
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Affiliation(s)
- Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome 00153, Italy
- Department of Translational and Precision Medicine, “Sapienza” University of Rome, Rome 00185, Italy
| | - Paraskevas Gkolfakis
- Department of Gastroenterology Hepatopancreatology and Digestive Oncology, Erasme University Hospital, Université Libre de Bruxelles, Brussels 1070, Belgium
| | - Georgios Tziatzios
- Hepatogastroenterology Unit, Second Department of Internal Medicine – Propaedeutic, Medical School, National and Kapodistrian University of Athens, ‘‘Attikon” University General Hospital, Athens 12462, Greece
| | - Ioannis S Papanikolaou
- Hepatogastroenterology Unit, Second Department of Internal Medicine – Propaedeutic, Medical School, National and Kapodistrian University of Athens, ‘‘Attikon” University General Hospital, Athens 12462, Greece
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Internal Medicine – Propaedeutic, Medical School, National and Kapodistrian University of Athens, ‘‘Attikon” University General Hospital, Athens 12462, Greece
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome 00153, Italy
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162
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Tontini GE, Neumann H. Artificial intelligence: Thinking outside the box. Best Pract Res Clin Gastroenterol 2020; 52-53:101720. [PMID: 34172247 DOI: 10.1016/j.bpg.2020.101720] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/01/2020] [Accepted: 12/03/2020] [Indexed: 01/31/2023]
Abstract
Artificial intelligence (AI) for luminal gastrointestinal endoscopy is rapidly evolving. To date, most applications have focused on colon polyp detection and characterization. However, the potential of AI to revolutionize our current practice in endoscopy is much more broadly positioned. In this review article, the Authors provide new ideas on how AI might help endoscopists in the future to rediscover endoscopy practice.
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Affiliation(s)
- Gian Eugenio Tontini
- Gastroenterology and Endoscopy Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, University Hospital Mainz, Mainz, Germany.
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163
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Antonelli G, Badalamenti M, Hassan C, Repici A. Impact of artificial intelligence on colorectal polyp detection. Best Pract Res Clin Gastroenterol 2020; 52-53:101713. [PMID: 34172246 DOI: 10.1016/j.bpg.2020.101713] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 11/30/2020] [Indexed: 01/31/2023]
Abstract
Since colonoscopy and polypectomy were introduced, Colorectal Cancer (CRC) incidence and mortality decreased significantly. Although we have entered the era of quality measurement and improvement, literature shows that a considerable amount of colorectal neoplasia is still missed by colonoscopists up to 25%, leading to an high rate of interval colorectal cancer that account for nearly 10% of all diagnosed CRC. Two main reasons have been recognised: recognition failure and mucosal exposure. For this purpose, Artificial Intelligence (AI) systems have been recently developed that identify a "hot" area during the endoscopic examination. In retrospective studies, where the systems are tested with a batch of unknown images, deep learning systems have shown very good performances, with high levels of accuracy. Of course, this setting may not reflect actual clinical practice where different pitfalls can occur, like suboptimal bowel preparation or poor examination technique. For this reason, a number of randomised clinical trials have recently been published where AI was tested in real time during endoscopic examinations. We present here an overview on recent literature addressing the performance of Computer Assisted Detection (CADe) of colorectal polyps in colonoscopy.
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Affiliation(s)
- Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Matteo Badalamenti
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano, 20089, Italy.
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano, 20089, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy
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164
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Mohan BP, Facciorusso A, Khan SR, Chandan S, Kassab LL, Gkolfakis P, Tziatzios G, Triantafyllou K, Adler DG. Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials. EClinicalMedicine 2020; 29-30:100622. [PMID: 33294821 PMCID: PMC7691740 DOI: 10.1016/j.eclinm.2020.100622] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/04/2020] [Accepted: 09/28/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Recent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs). METHODS Multiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I2% values. Outcomes assessed included other per patient adenoma parameters. FINDINGS Six RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3-1.72), p<0.0001, I2=56%; and pooled PDR, RR=1.42 (95% CI 1.33-1.51), p<0.00001, I2=9%; when compared to SC. Per patient adenoma detection parameters were significantly better with CADe colonoscopy when compared to SC, with increased scope withdrawal time (mean difference = 0.38, 95% CI 0.05-0.72, p = 0.02). INTERPRETATION Based on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.
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Affiliation(s)
- Babu P. Mohan
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Antonio Facciorusso
- Gastroenterology Unit, University of Foggia, Foggia, Italy
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Shahab R. Khan
- Gastroenterology, Rush University Medical Center, Chicago, IL, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Saurabh Chandan
- Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Lena L. Kassab
- Internal Medicine, Mayo Clinic, Rochester, MIN, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Paraskevas Gkolfakis
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Georgios Tziatzios
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Douglas G. Adler
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
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A Gratifying Step forward for the Application of Artificial Intelligence in the Field of Endoscopy: A Narrative Review. Surg Laparosc Endosc Percutan Tech 2020; 31:254-263. [PMID: 33122593 PMCID: PMC8132898 DOI: 10.1097/sle.0000000000000881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 09/22/2020] [Indexed: 12/24/2022]
Abstract
Endoscopy is the optimal choice of diagnosis of gastrointestinal (GI) diseases. Following the advancements made in medical technology, different kinds of novel endoscopy-methods have emerged. Although the significant progress in the penetration of endoscopic tools that have markedly improved the diagnostic rate of GI diseases, there are still some limitations, including instability of human diagnostic performance caused by intensive labor burden and high missed diagnosis rate of subtle lesions. Recently, artificial intelligence (AI) has been applied gradually to assist endoscopists in addressing these issues.
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Attardo S, Chandrasekar VT, Spadaccini M, Maselli R, Patel HK, Desai M, Capogreco A, Badalamenti M, Galtieri PA, Pellegatta G, Fugazza A, Carrara S, Anderloni A, Occhipinti P, Hassan C, Sharma P, Repici A. Artificial intelligence technologies for the detection of colorectal lesions: The future is now. World J Gastroenterol 2020; 26:5606-5616. [PMID: 33088155 PMCID: PMC7545398 DOI: 10.3748/wjg.v26.i37.5606] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 09/15/2020] [Indexed: 02/06/2023] Open
Abstract
Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.
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Affiliation(s)
- Simona Attardo
- Department of Endoscopy and Digestive Disease, AOU Maggiore della Carità, Novara 28100, Italy
| | | | - Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Harsh K Patel
- Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, LA 70124, United States
| | - Madhav Desai
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Antonio Capogreco
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Matteo Badalamenti
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | | | - Gaia Pellegatta
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Andrea Anderloni
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Pietro Occhipinti
- Department of Endoscopy and Digestive Disease, AOU Maggiore della Carità, Novara 28100, Italy
| | - Cesare Hassan
- Endoscopy Unit, Nuovo Regina Margherita Hospital, Roma 00153, Italy
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
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Parasa S, Wallace M, Bagci U, Antonino M, Berzin T, Byrne M, Celik H, Farahani K, Golding M, Gross S, Jamali V, Mendonca P, Mori Y, Ninh A, Repici A, Rex D, Skrinak K, Thakkar SJ, van Hooft JE, Vargo J, Yu H, Xu Z, Sharma P. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc 2020; 92:938-945.e1. [PMID: 32343978 DOI: 10.1016/j.gie.2020.04.044] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 04/16/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. METHODS A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. RESULTS There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. CONCLUSIONS Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.
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Affiliation(s)
- Sravanthi Parasa
- Department of Gastroenterology, Swedish Medical Center, Seattle, Washington, USA
| | - Michael Wallace
- Department of Medicine, Mayo Clinic, Director, Digestive Diseases Research Program, Editor in Chief Gastrointestinal Endoscopy, President, Florida Gastroenterology Society, Jacksonville, Florida, USA
| | - Ulas Bagci
- Artificial Intelligence in Medicine (AIM), Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA
| | - Mark Antonino
- Gastroenterology and Endoscopy Devices Team, Division of Renal, Gastrointestinal, Obesity and Transplant Devices, Office of Gastrorenal, ObGyn, General Hospital and Urology Devices, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tyler Berzin
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Michael Byrne
- Division of Gastroenterology, Vancouver General Hospital/University of British Columbia, Vancouver, British Columbia, Canada
| | - Haydar Celik
- Clinical Center, National Institutes of Health, Bethesda, Maryland, USA; George Washington University, Washington, DC, USA
| | - Keyvan Farahani
- Image-Guided Interventions and Imaging Informatics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Martin Golding
- Gastroenterology and Endoscopy Devices Team, Division of Renal, Gastrointestinal, Obesity and Transplant Devices, Office of Gastrorenal, ObGyn, General Hospital and Urology Devices, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Seth Gross
- Department of Medicine, Division of Gastroenterology, Clinical Care and Quality, NYU Langone Health, New York, New York, USA
| | - Vafa Jamali
- Respiratory, Gastrointestinal & Informatics, Medtronic Inc, Boulder, Colorado, USA
| | - Paulo Mendonca
- Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
| | | | | | - Alessandro Repici
- Digestive Endoscopy Unit, Humanitas, Research Hospital, Milan, Italy
| | - Douglas Rex
- Departments of Medicine, Endoscopy, and Gastroenterology, Indiana University of School of Medicine, Indianapolis, Indiana, USA
| | - Kris Skrinak
- Global Machine Learning Segment Lead, Amazon Web Services, New York, New York, USA
| | - Shyam J Thakkar
- Department of Endoscopy, Allegheny Health Network, Department of Medicine, Temple University, Philadelphia, Pennsylvania, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | | | - John Vargo
- Department of Medicine, Gastroenterology, Hepatology & Nutrition, Cleveland Clinic, Cleveland, Ohio, USA
| | - Honggang Yu
- Division of Gastroenterology, Renmin Hospital, Wuhan University, Wuhan, China
| | - Ziyue Xu
- Medical Image Analysis, NVIDIA, Bethesda, Maryland, USA
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, University of Kansas School of Medicine, Kansas City, Kansas, USA
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Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020; 2:e537-e548. [PMID: 33328048 PMCID: PMC8183333 DOI: 10.1016/s2589-7500(20)30218-1] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 08/20/2020] [Indexed: 02/06/2023]
Abstract
The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
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Affiliation(s)
- Xiaoxuan Liu
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK; Health Data Research UK, London, UK
| | - Samantha Cruz Rivera
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Melanie J Calvert
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Health Data Research UK, London, UK; National National Institute of Health Research Surgical Reconstruction and Microbiology Centre, and National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute of Health Research Applied Research Collaborative West Midlands, Birmingham, UK
| | - Alastair K Denniston
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK; Health Data Research UK, London, UK.
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Aziz M, Fatima R, Dong C, Lee-Smith W, Nawras A. The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis. J Gastroenterol Hepatol 2020; 35:1676-1683. [PMID: 32267558 DOI: 10.1111/jgh.15070] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/29/2020] [Accepted: 04/05/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIM The utility of artificial intelligence (AI) in colonoscopy has gained popularity in current times. Recent trials have evaluated the efficacy of deep convolutional neural network (DCNN)-based AI system in colonoscopy for improving adenoma detection rate (ADR) and polyp detection rate (PDR). We performed a systematic review and meta-analysis of the available studies to assess the impact of DCNN-based AI-assisted colonoscopy in improving the ADR and PDR. METHODS We queried the following database for this study: PubMed, Embase, Cochrane Library, Web of Sciences, and Computers and Applied Sciences. We only included randomized controlled trials that compared AI colonoscopy to standard colonoscopy (SC). Our outcomes included ADR and PDR. Risk ratios (RR) with 95% confidence interval (CI) were calculated using random effects model and DerSimonian-Laird approach for each outcome. RESULTS A total of three studies with 2815 patients (1415 in SC group and 1400 in AI group) were included. AI colonoscopy resulted in significantly improved ADR (32.9% vs 20.8%, RR: 1.58, 95% CI 1.39-1.80, P = < 0.001) and PDR (43.0% vs 27.8%, RR: 1.55, 95% CI 1.39-1.72, P = < 0.001) compared with SC. CONCLUSION Given the results and limitations, the utility of AI colonoscopy holds promise and should be evaluated in more randomized controlled trials across different population, especially in patients solely undergoing colonoscopy for screening purpose as improved ADR will ultimately help in reducing incident colorectal cancer.
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Affiliation(s)
- Muhammad Aziz
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Rawish Fatima
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Charles Dong
- Department of Internal Medicine, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Wade Lee-Smith
- University of Toledo Libraries, University of Toledo Medical Center, Toledo, Ohio, USA
| | - Ali Nawras
- Department of Gastroenterology, University of Toledo Medical Center, Toledo, Ohio, USA
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Lui TKL, Leung WK. Is artificial intelligence the final answer to missed polyps in colonoscopy? World J Gastroenterol 2020; 26:5248-5255. [PMID: 32994685 PMCID: PMC7504252 DOI: 10.3748/wjg.v26.i35.5248] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/30/2020] [Accepted: 08/25/2020] [Indexed: 02/06/2023] Open
Abstract
Lesions missed by colonoscopy are one of the main reasons for post-colonoscopy colorectal cancer, which is usually associated with a worse prognosis. Because the adenoma miss rate could be as high as 26%, it has been noted that endoscopists with higher adenoma detection rates are usually associated with lower adenoma miss rates. Artificial intelligence (AI), particularly the deep learning model, is a promising innovation in colonoscopy. Recent studies have shown that AI is not only accurate in colorectal polyp detection but can also reduce the miss rate. Nevertheless, the application of AI in real-time detection has been hindered by heterogeneity of the AI models and study design as well as a lack of long-term outcomes. Herein, we discussed the principle of various AI models and systematically reviewed the current data on the use of AI on colorectal polyp detection and miss rates. The limitations and future prospects of AI on colorectal polyp detection are also discussed.
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Affiliation(s)
- Thomas K L Lui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China
| | - Wai K Leung
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong, China
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Dong J, Geng Y, Lu D, Li B, Tian L, Lin D, Zhang Y. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov. Front Oncol 2020; 10:1629. [PMID: 33042806 PMCID: PMC7522504 DOI: 10.3389/fonc.2020.01629] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/27/2020] [Indexed: 02/05/2023] Open
Abstract
Objective: Clinical trials are the most effective way to judge the merits of diagnosis and treatment strategies. The in-depth mining of clinical trial data enables us to grasp the application trend of artificial intelligence (AI) for cancer diagnosis. The aim of this study was to analyze the characteristics of registered trials on AI for cancer diagnosis. Methods: Clinical trials on AI for cancer diagnosis registered on the ClinicalTrials.gov database were searched and downloaded. Statistical analysis was performed by using SPSS 20.0 software. Results: A total of 97 registered trials were included. Of them, only 27 (27.8%) were interventional trials and 70 (72.1%) were observational trials. Fifteen (15.4%) trials had been completed. Fifty trials were in recruitment, and another 18 remained unrecruited. The number of cases included in the clinical trials tended to be large, 31 (32.0%) trials including samples ranging from 100 to 499 cases and 17 (17.5%) trials including samples ranging from 500 to 999 cases. Of the 27 interventional trials, only two trials reported trials' phase. Most (85.2%) interventional trials were for diagnosis, and a few (3.7%) were for the purpose of both the diagnosis and therapy of cancers. For the observational clinical trials, 46 (65.7%) were cohort studies, and 11 (15.7%) were case-only studies. Among the observational trials, 46 (65.7%) were prospective studies and 13 (18.6%) were retrospective studies. Among 97 trials, 37 (38.1%) involved colorectal cancer, 11 (11.3%) involved breast cancer, 43 (44.3%) were for imaging diagnosis, 33 (34.0%) were for endoscopic diagnosis, and 11 (11.3%) were for pathological diagnosis. For the interventional trials, 11 trials were parallel assignment (40.7%), and 14 were single group assignment (51.9%). Among the 27 interventional trials, 18 (66.7%) trials were performed without masking, 6 (22.2%) trials were performed with single masking, only 1 (3.7%) was performed with double masking, and 2 (7.4%) was performed with triple masking. Conclusion: It appears that most registered trials on AI for cancer diagnosis are observational design, and more trials are needed in this field.
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Affiliation(s)
- Jingsi Dong
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yingcai Geng
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Lu
- Department of Otorhinolaryngology, Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bingjie Li
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Long Tian
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Lin
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
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Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26:5090-5100. [PMID: 32982111 PMCID: PMC7495038 DOI: 10.3748/wjg.v26.i34.5090] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 08/12/2020] [Indexed: 02/06/2023] Open
Abstract
Since the advent of artificial intelligence (AI) technology, it has been constantly studied and has achieved rapid development. The AI assistant system is expected to improve the quality of automatic polyp detection and classification. It could also help prevent endoscopists from missing polyps and make an accurate optical diagnosis. These functions provided by AI could result in a higher adenoma detection rate and decrease the cost of polypectomy for hyperplastic polyps. In addition, AI has good performance in the staging, diagnosis, and segmentation of colorectal cancer. This article provides an overview of recent research focusing on the application of AI in colorectal polyps and cancer and highlights the advances achieved.
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Affiliation(s)
- Ke-Wei Wang
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
| | - Ming Dong
- Department of Gastrointestinal Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China
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Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ 2020; 370:m3210. [PMID: 32907797 PMCID: PMC7490785 DOI: 10.1136/bmj.m3210] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/04/2020] [Indexed: 02/06/2023]
Abstract
The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes.The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases.SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
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Affiliation(s)
- Samantha Cruz Rivera
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Xiaoxuan Liu
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, UK
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Research Institute, Women's College Hospital, University of Toronto, Ontario, Canada
| | - Alastair K Denniston
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, UK
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- National Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK
| | - Melanie J Calvert
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Health Data Research UK, London, UK
- National Institute of Health Research Surgical Reconstruction and Microbiology Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Applied Research Collaborative West Midlands, Birmingham, UK
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Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. LANCET DIGITAL HEALTH 2020; 2:e549-e560. [PMID: 33328049 PMCID: PMC8212701 DOI: 10.1016/s2589-7500(20)30219-3] [Citation(s) in RCA: 125] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 08/20/2020] [Indexed: 12/13/2022]
Abstract
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial.
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Affiliation(s)
- Samantha Cruz Rivera
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Xiaoxuan Liu
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK; Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Research Institute, Women's College Hospital, University of Toronto, Toronto, ON, Canada
| | - Alastair K Denniston
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK; Health Data Research UK, London, UK.
| | - Melanie J Calvert
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK; Health Data Research UK, London, UK; National Institute of Health Research Surgical Reconstruction and Microbiology Centre, and National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; National Institute of Health Research Applied Research Collaborative West Midlands, Birmingham, UK
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Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ 2020; 370:m3164. [PMID: 32909959 PMCID: PMC7490784 DOI: 10.1136/bmj.m3164] [Citation(s) in RCA: 160] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/04/2020] [Indexed: 02/07/2023]
Abstract
The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes.The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants).The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases.CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
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Affiliation(s)
- Xiaoxuan Liu
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, UK
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Health Data Research UK, London, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Melanie J Calvert
- Health Data Research UK, London, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute of Health Research Surgical Reconstruction and Microbiology Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Applied Research Collaborative West Midlands, Birmingham, UK
| | - Alastair K Denniston
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, UK
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Health Data Research UK, London, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcome Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK
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178
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Zilli A, Capogreco A, Furfaro F, Allocca M, Roda G, Loy L, Fiorino G, Danese S. Improving quality of care in endoscopy of inflammatory bowel disease: can we do better? Expert Rev Gastroenterol Hepatol 2020; 14:819-828. [PMID: 32543983 DOI: 10.1080/17474124.2020.1780913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Endoscopy plays a key role in the management of inflammatory bowel disease (IBD). There is an increased need for quality assurance programs that evaluate the quality, safety and patient experiences of endoscopy, by assessing procedural and clinical outcomes. AREAS COVERED This review aims to summarize the most important quality indicators of endoscopy in IBD patients and could serve as the basis to improve quality endoscopic procedures and patients' perception of endoscopy in the future. However, further studies and consensus reports are necessary to standardize the quality of care in the endoscopy unit of all IBD centers. EXPERT COMMENTARY Developing an understanding of the patient-reported perception is important for both clinicians and patients, as it facilitates patient engagement with their care. Moreover, implementing education in reporting is crucial f and the use of verifiable databases, generated from electronic reporting systems, should be encouraged rather than unverified self-reporting, to have greater validity for documenting and to formally evaluate endoscopic practice data with audits. The use of artificial intelligence may improve the quality of endoscopy, by increasing the adenoma detection rate and helping endoscopists in the challenging differentiation between inflammatory and neoplastic lesions.
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Affiliation(s)
- Alessandra Zilli
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy
| | - Antonio Capogreco
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy
| | - Federica Furfaro
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy
| | - Mariangela Allocca
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy.,Department of Biomedical Sciences, Humanitas University , Milan, Italy
| | - Giulia Roda
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy
| | - Laura Loy
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy
| | - Gionata Fiorino
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy.,Department of Biomedical Sciences, Humanitas University , Milan, Italy
| | - Silvio Danese
- IBD Center, Department of Gastroenterology, Humanitas Clinical and Research Institute , Milan, Italy.,Department of Biomedical Sciences, Humanitas University , Milan, Italy
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179
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Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 2020; 26:1351-1363. [PMID: 32908284 PMCID: PMC7598944 DOI: 10.1038/s41591-020-1037-7] [Citation(s) in RCA: 207] [Impact Index Per Article: 51.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023]
Abstract
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
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Affiliation(s)
- Samantha Cruz Rivera
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Xiaoxuan Liu
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Health Data Research UK, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Research Institute, Women's College Hospital, University of Toronto, Ontario, Canada
| | - Alastair K Denniston
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Health Data Research UK, London, UK.
- National Institute of Health Research Biomedical Research Centre for Ophthalmology, Moorfields Hospital London NHS Foundation Trust and University College London, Institute of Ophthalmology, London, UK.
| | - Melanie J Calvert
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Health Data Research UK, London, UK
- National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Applied Research Collaborative West Midlands, Coventry, UK
- National Institute of Health Research Surgical Reconstruction and Microbiology Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
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180
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Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 2020; 26:1364-1374. [PMID: 32908283 PMCID: PMC7598943 DOI: 10.1038/s41591-020-1034-x] [Citation(s) in RCA: 335] [Impact Index Per Article: 83.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 07/23/2020] [Indexed: 02/07/2023]
Abstract
The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
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Affiliation(s)
- Xiaoxuan Liu
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Health Data Research UK, London, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada
| | - Melanie J Calvert
- Health Data Research UK, London, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- National Institute of Health Research Birmingham Biomedical Research Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute of Health Research Applied Research Collaborative West Midlands, Coventry, UK
- National Institute of Health Research Surgical Reconstruction and Microbiology Centre, University of Birmingham and University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Alastair K Denniston
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Health Data Research UK, London, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
- Centre for Patient Reported Outcomes Research, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
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181
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificial intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020; 1:6-18. [DOI: 10.37126/aige.v1.i1.6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 07/07/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) allows machines to provide disruptive value in several industries and applications. Applications of AI techniques, specifically machine learning and more recently deep learning, are arising in gastroenterology. Computer-aided diagnosis for upper gastrointestinal endoscopy has growing attention for automated and accurate identification of dysplasia in Barrett’s esophagus, as well as for the detection of early gastric cancers (GCs), therefore preventing esophageal and gastric malignancies. Besides, convoluted neural network technology can accurately assess Helicobacter pylori (H. pylori) infection during standard endoscopy without the need for biopsies, thus, reducing gastric cancer risk. AI can potentially be applied during colonoscopy to automatically discover colorectal polyps and differentiate between neoplastic and non-neoplastic ones, with the possible ability to improve adenoma detection rate, which changes broadly among endoscopists performing screening colonoscopies. In addition, AI permits to establish the feasibility of curative endoscopic resection of large colonic lesions based on the pit pattern characteristics. The aim of this review is to analyze current evidence from the literature, supporting recent technologies of AI both in upper and lower gastrointestinal diseases, including Barrett's esophagus, GC, H. pylori infection, colonic polyps and colon cancer.
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Affiliation(s)
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto G. Giglio, Cefalù 90015, Italy
| | - Alessandro Vitello
- Gastroenterology and Endoscopy Unit, S. Elia- M. Raimondi Hospital, Caltanissetta 93100, Italy
| | - Endrit Shahini
- Gastroenterology and Endoscopy Unit, Istituto di Candiolo, FPO-IRCCS, Candiolo (Torino) 93100, Italy
| | | | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia- M. Raimondi Hospital, Caltanissetta 93100, Italy
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182
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Morreale GC, Sinagra E, Vitello A, Shahini E, Shahini E, Maida M. Emerging artificia intelligence applications in gastroenterology: A review of the literature. Artif Intell Gastrointest Endosc 2020. [DOI: 10.37126/wjem.v1.i1.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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183
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The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future. ACTA ACUST UNITED AC 2020; 56:medicina56070364. [PMID: 32708343 PMCID: PMC7404688 DOI: 10.3390/medicina56070364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023]
Abstract
In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.
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184
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García-Peraza-Herrera LC, Everson M, Lovat L, Wang HP, Wang WL, Haidry R, Stoyanov D, Ourselin S, Vercauteren T. Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology. Int J Comput Assist Radiol Surg 2020; 15:651-659. [PMID: 32166574 PMCID: PMC7142046 DOI: 10.1007/s11548-020-02127-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 02/17/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy. METHODS We present a new benchmark dataset containing 68K binary labelled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network architecture solves the binary classification task and explains what features of the input domain drive the decision-making process of the network. RESULTS The proposed method achieved an average accuracy of 91.7% compared to the 94.7% achieved by a group of 12 senior clinicians. Our novel network architecture produces deeply supervised activation heatmaps that suggest the network is looking at intrapapillary capillary loop patterns when predicting abnormality. CONCLUSION We believe that this dataset and baseline method may serve as a reference for future benchmarks on both video frame classification and explainability in the context of ESCN detection. A future work path of high clinical relevance is the extension of the classification to ESCN types.
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Affiliation(s)
- Luis C García-Peraza-Herrera
- Department of Medical Physics and Biomedical Engineering, UCL, London, UK.
- School of Biomedical Engineering and Imaging Science, KCL, London, UK.
| | - Martin Everson
- Division of Surgery and Interventional Science, UCL, London, UK
- Department of Gastroenterology, University College Hospital NHS Foundation Trust, London, UK
| | - Laurence Lovat
- Division of Surgery and Interventional Science, UCL, London, UK
- Department of Gastroenterology, University College Hospital NHS Foundation Trust, London, UK
| | - Hsiu-Po Wang
- Department of Internal Medicine, National Taiwan University, Taipei, Taiwan
| | - Wen Lun Wang
- Department of Internal Medicine, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan
| | - Rehan Haidry
- Division of Surgery and Interventional Science, UCL, London, UK
- Department of Gastroenterology, University College Hospital NHS Foundation Trust, London, UK
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, UCL, London, UK
| | | | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Science, KCL, London, UK
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185
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Siri here, cecum reached, but please wash that fold: Will artificial intelligence improve gastroenterology? Gastrointest Endosc 2020; 91:425-427. [PMID: 32036947 DOI: 10.1016/j.gie.2019.10.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 10/18/2019] [Indexed: 12/11/2022]
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186
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Gulati S, Emmanuel A, Patel M, Williams S, Haji A, Hayee B, Neumann H. Artificial intelligence in luminal endoscopy. Ther Adv Gastrointest Endosc 2020; 13:2631774520935220. [PMID: 32637935 PMCID: PMC7315657 DOI: 10.1177/2631774520935220] [Citation(s) in RCA: 6] [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: 01/16/2020] [Accepted: 05/22/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence is a strong focus of interest for global health development. Diagnostic endoscopy is an attractive substrate for artificial intelligence with a real potential to improve patient care through standardisation of endoscopic diagnosis and to serve as an adjunct to enhanced imaging diagnosis. The possibility to amass large data to refine algorithms makes adoption of artificial intelligence into global practice a potential reality. Initial studies in luminal endoscopy involve machine learning and are retrospective. Improvement in diagnostic performance is appreciable through the adoption of deep learning. Research foci in the upper gastrointestinal tract include the diagnosis of neoplasia, including Barrett's, squamous cell and gastric where prospective and real-time artificial intelligence studies have been completed demonstrating a benefit of artificial intelligence-augmented endoscopy. Deep learning applied to small bowel capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. Prospective evaluation including the first randomised trial has been performed in the colon, demonstrating improved polyp and adenoma detection rates; however, these appear to be relevant to small polyps. There are potential additional roles of artificial intelligence relevant to improving the quality of endoscopic examinations, training and triaging of referrals. Further large-scale, multicentre and cross-platform validation studies are required for the robust incorporation of artificial intelligence-augmented diagnostic luminal endoscopy into our routine clinical practice.
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Affiliation(s)
- Shraddha Gulati
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Andrew Emmanuel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Mehul Patel
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Sophie Williams
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Amyn Haji
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Bu’Hussain Hayee
- King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Helmut Neumann
- Department of Interdisciplinary Endoscopy, University Hospital Mainz, 55131 Mainz, Germany
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