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Comparison with other studies_
| Method | Image type | AI technique used | Total images (TI) | Evaluation metric | Validation performance (VP) | |
|---|---|---|---|---|---|---|
| Hadjiyski (2020) | CT scans | Inception v3 | 4,200 | AUC | 86% | 0.02 |
| Aubreville et al. (2020) | Whole Slide Images | RetinaNet with ResNet-50 | 13,907 | F1 score | 79.1% | 0.01 |
| Wang et al. (2018) | Multi parametric MRI | V-net | 79 cases in total. About 790 images | Accuracy | 89.4% | 0.11 |
| Chung et al. (2015) | Multi parametric MRI | SVM with RD-CRF | 20 cases in total. About 200 images | Accuracy | 59% | 0.29 |
| Brunese et al. (2020) | Chest X-ray | VGG-16 | 9,326 | Accuracy | 98% | 0.01 |
| Wu et al. (2021) | Chest CT scan | VGG-16 with segmentation | 3,855 | Sensitivity | 95% | 0.03 |
| This study | Live partial robotic nephrectomy | Object detection with VGG-16 | 143 | Accuracy | 84% | 0.59 |
Result comparison_
| Detection algorithm | Precision | Recall | Mean average precision | Frames per second |
|---|---|---|---|---|
| YOLOv3 on virtual machine | 0.88 | 0.62 | 0.758 | Not applicable |
| YOLOv4 on windows | 0.98 | 0.99 | 0.974 | 21.4 |
Institutions that produced the videos_
| Source | Country | State |
|---|---|---|
| Brigham and Women’s Hospital | USA | Massachusetts |
| Seattle Science Foundation | USA | Washington |
| Pacific Northwest Urology specialist | USA | Washington |
| Vattikuti Foundation | USA | Michigan |
| Urologic Surgeons of Washington | USA | Washington |
Train, validation, test division_
| Dataset | Label | Training | Validation | Test |
|---|---|---|---|---|
| 1st dataset | Cancerous tissue | 30 | 9 | 5 |
| Non-cancerous tissue | 40 | 13 | 9 | |
| Fatty tissue | 21 | 10 | 6 | |
| 2nd dataset | Cancerous tissue | 105 | 9 | 5 |
| Non-cancerous tissue | 105 | 13 | 9 | |
| Fatty tissue | 105 | 10 | 6 | |
| 3rd dataset | Cancerous tissue | 150 | 9 | 5 |
| Non-cancerous tissue | 150 | 23 | 15 |