Endoscopic prediction of deeply submucosal invasive carcinoma with use of artificial intelligence
| Autor: | Thomas K.L. Lui, Kenneth K.Y. Wong, Loey L.Y. Mak, Michael K.L. Ko, Stephen K.K. Tsao, Wai K. Leung |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Endoscopy International Open, Vol 07, Iss 04, Pp E514-E520 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2364-3722 2196-9736 |
| DOI: | 10.1055/a-0849-9548 |
| Popis: | Background and study aims We evaluated use of artificial intelligence (AI) assisted image classifier in determining the feasibility of curative endoscopic resection of large colonic lesion based on non-magnified endoscopic images Methods AI image classifier was trained by 8,000 endoscopic images of large (≥ 2 cm) colonic lesions. The independent validation set consisted of 567 endoscopic images from 76 colonic lesions. Histology of the resected specimens was used as gold standard. Curative endoscopic resection was defined as histology no more advanced than well-differentiated adenocarcinoma, ≤ 1 mm submucosal invasion and without lymphovascular invasion, whereas non-curative resection was defined as any lesion that could not meet the above requirements. Performance of the trained AI image classifier was compared with that of endoscopists. Results In predicting endoscopic curative resection, AI had an overall accuracy of 85.5 %. Images from narrow band imaging (NBI) had significantly higher accuracy (94.3 % vs 76.0 %; P |
| Databáze: | Directory of Open Access Journals |
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