Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps
| Title: | Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps |
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| Authors: | Toshio Uraoka, Shiko Kuribayashi, Yu Hashimoto, Yoji Takeuchi, Keigo Sato, Mizuki Kuramochi, Akihiko Tsuchiya, Akihiro Yamaguchi, Yasuo Hosoda, Norio Yamaguchi, Naohiro Nakamura, Yuki Itoi, Kengo Kasuga, Hirohito Tanaka |
| Source: | BMJ Open Gastroenterology, Vol 11, Iss 1 (2024) |
| Publisher Information: | BMJ Publishing Group, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Diseases of the digestive system. Gastroenterology |
| Subject Terms: | Diseases of the digestive system. Gastroenterology, RC799-869 |
| More Details: | Objective Computer-aided diagnosis (CAD) using artificial intelligence (AI) is expected to support the characterisation of colorectal lesions, which is clinically relevant for efficient colorectal cancer prevention. We conducted this study to assess the diagnostic performance of commercially available CAD systems.Methods This was a multicentre, prospective performance evaluation study. The endoscopist diagnosed polyps using white light imaging, followed by non-magnified blue light imaging (non-mBLI) and mBLI. AI subsequently assessed the lesions using non-mBLI (non-mAI), followed by mBLI (mAI). Eventually, endoscopists made the final diagnosis by integrating the AI diagnosis (AI+endoscopist). The primary endpoint was the accuracy of the AI diagnosis of neoplastic lesions. The diagnostic performance of each modality (sensitivity, specificity and accuracy) and confidence levels were also assessed.Results Overall, 380 lesions from 139 patients were included in the analysis. The accuracy of non-mAI was 83%, 95% CI (79% to 87%), which was inferior to that of mBLI (89%, 95% CI (85% to 92%)) and mAI (89%, 95% CI (85% to 92%)). The accuracy (95% CI) of diagnosis by expert endoscopists using mAI (91%, 95% CI (87% to 94%)) was comparable to that of expert endoscopists using mBLI (91%, 95% CI (87% to 94%)) but better than that of non-expert endoscopists using mAI (83%, 95% CI (75% to 90%)). The level of confidence in making a correct diagnosis was increased when using magnification and AI.Conclusions The diagnostic performance of mAI for differentiating colonic lesions is comparable to that of endoscopists, regardless of their experience. However, it can be affected by the use of magnification as well as the endoscopists’ level of experience. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2024-0015 2054-4774 |
| Relation: | https://bmjopengastro.bmj.com/content/11/1/e001553.full; https://doaj.org/toc/2054-4774 |
| DOI: | 10.1136/bmjgast-2024-001553 |
| Access URL: | https://doaj.org/article/9130c18c535f444dbc6e4946b805d7c9 |
| Accession Number: | edsdoj.9130c18c535f444dbc6e4946b805d7c9 |
| Database: | Directory of Open Access Journals |
| ISSN: | 20240015 20544774 |
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| DOI: | 10.1136/bmjgast-2024-001553 |
| Published in: | BMJ Open Gastroenterology |
| Language: | English |