Copyright
©The Author(s) 2020.
Artif Intell Gastrointest Endosc. Jul 28, 2020; 1(1): 19-27
Published online Jul 28, 2020. doi: 10.37126/aige.v1.i1.19
Published online Jul 28, 2020. doi: 10.37126/aige.v1.i1.19
Ref. | Training | Validation | AUC | Sensitivity | Accuracy |
Chen et al[51], 2018 | 1476 images of neoplasms; 681 images of H. pylori | 188 images of neoplasms; 96 images of H. pylori | NA | 96.3% | 90.1% |
Urban et al[16], 2018 | 8641 images; 9 videos | 1330 images; 9 videos | 0.974 | NA | 96.4% |
Misawa et al[15], 2018 | 73 videos | Cross validation | NA | 90% | 76.5% |
Yamada et al[56], 2019 | 4087 images of polyps; videos | 705 images with polyps; 4135 images without polyps | 0.975 | 97.3% | NA |
Klare et al[57], 2019 | NA | 55 colonoscopy examination videos | NA | 75.3% | NA |
Wang et al[17], 2019 | 3634 images with polyps; 1911 images without polyps | 5541 images with polyps and 21572 images without polyps | 0.984 | 94.4% | NA |
Song et al[58], 2020 | 12480 images | 545 images | 0.93 | 82.1% | 81.3% |
Zachariah et al[59], 2020 | 8246 images | 634 images | NA | 96% | 94% |
- Citation: Jin HY, Zhang M, Hu B. Techniques to integrate artificial intelligence systems with medical information in gastroenterology. Artif Intell Gastrointest Endosc 2020; 1(1): 19-27
- URL: https://www.wjgnet.com/2689-7164/full/v1/i1/19.htm
- DOI: https://dx.doi.org/10.37126/aige.v1.i1.19