Table 1.
Clinical Studies of Artificial Intelligence for the Detection of Colorectal Polyps
| Author (year) | Study design | Algorithm type | Dataset | Processing time | Results |
|---|---|---|---|---|---|
| Wang et al. (2019) 21 |
Randomized controlled study |
Convolutional neural network | 5,545 Images | 25 fps with 77 ms latency | 9% Increase of ADR |
| Klare et al. (2019) 22 |
Prospective In vivo |
Convolutional neural network | 55 Live colonoscopies | 50 ms latency | Sensitivity 75%/polyp ADR 29% (31% in endoscopist) |
| Urban et al. (2018) 20 |
Retrospective Ex vivo |
Convolutional neural network | Image dataset: 8,641 polyps Video: 20 colonoscopies |
10 ms/frame (real-time) |
Image dataset: accuracy 96.4% AUROC 0.991 |
| Misawa et al. (2018)19 |
Retrospective Ex vivo |
Convolutional neural network | 135 Video clips | No description | Sensitivity 90% Specificity 63.3% Accuracy 76.5% |
| Zhang et al. (2017) 23 |
Retrospective Ex vivo |
Convolutional neural network | 150 Random+30 NBI images | No description | Sensitivity 98% PPV 99% AUROC 1.00 |
| Yu et al. (2017) 24 |
Retrospective Ex vivo |
Convolutional neural network | ASU-Mayo 20 videos | 1.23 s/frame | Sensitivity 7% PPV 88% |
| Angermann et al. (2017) 25 | Retrospective Ex vivo |
Hand-crafted | No description | 20–185 ms 0.3-1.8 s delay |
Sensitivity 100%/polyp PPV50% |
| Tajbakhsh et al. (2015) 26 | Retrospective Ex vivo |
Hand-crafted | No description | 2.6 s/frame | Sensitivity 48% on proprietary database Sensitivity 88% in CVC-colon DB |
| Karkanis et al. (2003) 18 | Retrospective Ex vivo |
Hand-crafted | 180 Still images | 1.5 m/video | Sensitivity 94% Specificity 99% |
ADR, adenoma detection rate; AUROC, area under the receiver operating characteristics; NBI, narrow band imaging; PPV, positive predictive value; ASU, Arizona State University; CVC, computer vision center; DB, data base.