Copyright
©The Author(s) 2020.
World J Gastroenterol. Sep 14, 2020; 26(34): 5090-5100
Published online Sep 14, 2020. doi: 10.3748/wjg.v26.i34.5090
Published online Sep 14, 2020. doi: 10.3748/wjg.v26.i34.5090
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 |
Ref. | Patients, n | Samples, n | Sensitivity, % | Specificity, % | Accuracy, % | NPV, % | PPV, % |
Mori et al[13] | 152 | 176 | 92.0 | 79.5 | 89.2 | - | |
Misawa et al[14] | - | 100 | 84.5 | 97.6 | 90.0 | 82.0 | 98.0 |
Kominami et al[15] | 41 | 118 | 95.9 | 93.3 | 94.9 | 93.3 | 95.9 |
Mori et al[16] | 123 | 205 | 89.0 | 88.0 | 89.0 | 76.0 | 95.0 |
Misawa et al[17] | 58 | 64 | 94.3 | 71.4 | 87.8 | 83.3 | 89.2 |
Chen et al[18] | 193 | 284 | 96.3 | 78.1 | 90.1 | 91.5 | 89.6 |
Misawa et al[19] | 73 | 155 | 90.0 | 63.3 | 76.5 | - | - |
Shin et al[20] | - | 366 | 95.9 | 95.9 | 95.9 | - | 96.4 |
Wang et al[21] | 1138 | 27113 | 94.4 | 95.9 | - | - | - |
Kudo et al[22] | 89 | 100 | 96.9 (stained) | 100.0 | 98.0 | 94.6 | 100.0 |
96.9 (NBI) | 94.3 | 96.0 | 94.3 | 96.9 | |||
Min et al[23] | 91 | 181 | 83.3 | 70.1 | 78.4 | 71.2 | 82.6 |
Sánchez-Montes et al[24] | - | 225 | 92.3 | 89.2 | 91.1 | 87.1 | 93.6 |
Horiuchi et al[25] | 77 | 258 | 80.0 | 95.3 | 91.5 | 93.4 | 85.2 |
Byrne et al[26] | - | 106 | 98.0 | 83.0 | 94.0 | 97.0 | 90.0 |
- Citation: Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol 2020; 26(34): 5090-5100
- URL: https://www.wjgnet.com/1007-9327/full/v26/i34/5090.htm
- DOI: https://dx.doi.org/10.3748/wjg.v26.i34.5090