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Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia

Ahmad, OF; González-Bueno Puyal, J; Brandao, P; Kader, R; Abbasi, F; Hussein, M; Haidry, RJ; ... Lovat, LB; + view all (2021) Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia. Digestive Endoscopy 10.1111/den.14187. (In press). Green open access

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Abstract

OBJECTIVES: There is uncertainty regarding the efficacy of artificial intelligence (AI) software to detect advanced subtle neoplasia, particularly flat lesions and sessile serrated lesions (SSLs), due to low prevalence in testing datasets and prospective trials. This has been highlighted as a top research priority for the field. METHODS: An AI algorithm was evaluated on 4 video test datasets containing 173 polyps (35,114 polyp positive frames and 634,988 polyp-negative frames) specifically enriched with flat lesions and SSLs, including a challenging dataset containing subtle advanced neoplasia. The challenging dataset was also evaluated by 8 endoscopists (4 independent, 4 trainees, according to Joint Advisory Group on GI endoscopy (JAG) standards in United Kingdom). RESULTS: In the first 2 video datasets, the algorithm achieved per-polyp sensitivities of 100% and 98.9%. Per-frame sensitivities were 84.1% and 85.2% . In the subtle dataset, the algorithm detected a significantly higher number of polyps (P<0.0001), compared to JAG-independent and trainee endoscopists, achieving per-polyp sensitivities of 79.5%, 37.2% and 11.5% respectively. Furthermore, when considering subtle polyps detected by both the algorithm and at least one endoscopist, the AI detected polyps significantly faster on average. CONCLUSIONS: The AI based algorithm achieved high per-polyp sensitivities for advanced colorectal neoplasia, including flat lesions and SSLs, outperforming both JAG independent and trainees on a very challenging dataset containing subtle lesions that could have been overlooked easily and contribute to interval colorectal cancer. Further prospective trials should evaluate AI to detect subtle advanced neoplasia in higher risk populations for colorectal cancer.

Type: Article
Title: Performance of artificial intelligence for detection of subtle and advanced colorectal neoplasia
Location: Australia
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/den.14187
Publisher version: https://doi.org/10.1111/den.14187
Language: English
Additional information: © 2021 The Authors. Digestive Endoscopy published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Artificial intelligence, colonic polyps, colonoscopy,colorectal neoplasms, deep learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10138648
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