Copyright: ©Author(s) 2026.
World J Gastroenterol. Apr 21, 2026; 32(15): 114778
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.114778
Published online Apr 21, 2026. doi: 10.3748/wjg.v32.i15.114778
Table 4 Patient-level performance of doctors and model 96 in the internal test cohort (n = 37)
| Item | Accuracy (95%CI) | P value1 | Sensitivity (95%CI) | P value | Specificity (95%CI) | P value | Youden index | Kappa value |
| Residents of internal medicine | 0.541 (0.369-0.705) | 0.002b | 0.750 (0.349-0.968) | 1.000 | 0.483 (0.294-0.675) | 0.003b | 0.233 | 0.147 |
| Attending physicians | 0.730 (0.559-0.862) | 0.227 | 0.750 (0.349-0.968) | 1.000 | 0.724 (0.528-0.873) | 0.289 | 0.474 | 0.373 |
| Residents of radiology | 0.676 (0.502-0.820) | 0.039a | 0.875 (0.473-0.997) | 1.000 | 0.621 (0.423-0.793) | 0.039a | 0.496 | 0.341 |
| Attending radiologists | 0.865 (0.712-0.955) | 1.000 | 0.625 (0.245-0.915) | 0.500 | 0.931 (0.772-0.992) | 0.687 | 0.556 | 0.582 |
| Model 96 | 0.865 (0.712-0.955) | 0.875 (0.473-0.997) | 0.862 (0.683-0.961) | 0.737 | 0.649 |
- Citation: Wang SY, Yin SQ, Yang JY, Ji MY, Zeng XQ, Rao SX, Lv MZ, Bao J, Wang MN, Gao H. Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images. World J Gastroenterol 2026; 32(15): 114778
- URL: https://www.wjgnet.com/1007-9327/full/v32/i15/114778.htm
- DOI: https://dx.doi.org/10.3748/wjg.v32.i15.114778