Table 1.
Characteristics of studies on artificial intelligence in the detection and classification of colorectal polyps
| Ref. | Study type | Algorithm | Imaging modality | Image type | Training set | Testing set | Processing time |
| Mori et al[13] | Pilot study | - | EC | Real-time | - | - | 0.3 s/image |
| Misawa et al[14] | Ex vivo | Machine learning: SVM | EC, NBI | Still | 979 images (381 non-neoplasms, 598 neoplasms) | 100 images (50 non-neoplasms, 50 neoplasms) | 0.3 s/image |
| Kominami et al[15] | - | Machine learning: SVM | Colonoscopy, NBI | Real-time | 2247 cutout training images from 1262 colorectal lesions | 118 images | 20 frame/s |
| Mori et al[16] | International web-based trial | Machine learning: SVM | EC | Still | 6051 endocytoscopic images | 205 small polyps (147 neoplastic and 58 non-neoplastic) | 0.2 s/image |
| Misawa et al[17] | Pilot study | Machine learning: SVM | EC, NBI | Still | 1661 EC-NBI images (1213 neoplasm images, 448 non-neoplastic images) | 124 (19 neoplastic and 105 non-neoplastic) | - |
| Chen et al[18] | Pilot study | Deep neural network | Colonoscopy, magnifying NBI | Still | 2157 (1476 neoplastic polyps vs 681 hyperplastic polyps) | 284 (96 hyperplastic and 188 neoplastic polyps) | 0.45 s/image |
| Misawa et al[19] | Ex vivo | Machine learning | Colonoscopy, WL | Video | 411 (105 positive and 306 negative) | 135 (50 positive and 85 negative) | - |
| Shin et al[20] | Pilot study | Machine learning | Colonoscopy, WL | Video | 1525 (561 polyp patches and 964 normal patches) | 366 (196 polyp patches and 170 normal patches) | 95 ms/frame |
| Wang et al[21] | Ex vivo | Deep learning | Colonoscopy, WL | Still | 5545 (3634 images contained polyps and 1911 images did not contain polyps) | 27 113 (5541 images contained polyps and 21572 images did not contain polyps) | - |
| Kudo et al[22] | Pilot study | Texture analysis | EC stained or NBI image | Still | 69 142 EC images (43197 stained images and 25945 NBI images) | 100 polyps | 0.4 s/image |
| Min et al[23] | Pilot study | Gaussian mixture model | Colonoscopy, linked color imaging | Still | 139 images of adenomatous polyps and 69 images of non-adenomatous polyps | 115 images of adenomatous polyps and 66 images of non-adenomatous polyps | - |
| Sánchez-Montes et al[24] | Pilot study | SVM | Colonoscopy, WL | Still | - | - | - |
| Horiuchi et al[25] | Pilot study | - | Colonoscopy, autofluorescence imaging | Real-time | - | - | - |
| Byrne et al[26] | Ex vivo | Convolutional neural network | EC, NBI | Video | 223 polyp videos | 125 polyp videos | 50 ms/frame |
EC: Endocytoscopic images; SVM: Support vector machine; NBI: Narrow-band imaging; WL: White light.