Table 2.
Clinical Studies of Artificial Intelligence for Characterization of Colorectal Polyps
| Author (year) | Study design | Classification target and base | Algorithm type | Image modality | Dataset | Results |
|---|---|---|---|---|---|---|
| Byrne et al. (2019) 35 | Retrospective | Histology of diminutive polyp | Convolutional neural network | NBI video frames | 125 Diminutive polyp videos | Sensitivity 98% Specificity 83% Accuracy 94% |
| Chen et al. (2018) 36 | Retrospective | Neoplastic or hyperplastic polyp <5 mm | Convolutional neural network | Magnifying NBI | 284 Diminutive polyps image | Sensitivity 96.3% Specificity 78.1% Accuracy 90.1% |
| Mori et al. (2018) 44 | Prospective | Diagnosis of neoplastic diminutive polyp | SVM | Endocytoscopy with NBI and stained images | 466 Diminutive polyps from 325 patients |
Prediction rate 98.1% |
| Takeda et al. (2017) 43 | Retrospective | Invasive CRC | SVM | Endocytoscopy with NBI and stained images | 200 Images | Sensitivity 89.4% Specificity 98.9% Accuracy 94.1% |
| Kominami et al. (2016) 33 | Prospective | Histology | SVM with logistic regression | Magnifying NBI | 118 Colorectal lesions | Sensitivity 95.9% Specificity 93.3% Accuracy 94.9% |
| Misawa et al. (2016) 46 | Retrospective | Microvascular findings | SVM | Endocytoscopy with NBI | 100 Images | Sensitivity 84.5% Specificity 97.6% Accuracy 90.0% |
| Mori et al. (2015) 42 | Retrospective | Neoplastic changes in small polyps | Multivariate regression analysis | Endocytoscopy | 176 Polyps from 152 patients | Sensitivity 92% Specificity 79.5% Accuracy 89.2% |
| Takemura et al. (2012) 57 | Retrospective | Pit pattern | SVM | Magnifying NBI | 371 Images | Sensitivity 97.8% Specificity 97.9% Accuracy 97.8% |
| Gross et al. (2011) 32 | Prospective | Small colonic polyp <10 mm | SVM | Magnifying NBI | 434 Polyps from 214 patients | Sensitivity 95% Specificity 90.3% Accuracy 93.1% |
| Tischendorf et al. (2010) 31 | Prospective pilot |
Vascularization features | SVM | Magnifying NBI | 209 Polyps from 128 patients | Sensitivity 90% Specificity 70.2% Accuracy 85.3% |
| Takemura et al. (2010) 39 | Retrospective | Pit pattern | HuPAS software version 1.3 | Magnifying NBI with chromoendoscopy (crystal violet) | 134 Images | Accuracy 98.5% |
NBI, narrow band imaging; SVM, support vector machine; CRC, colorectal cancer.