Primary study · included by 1 review
An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network.
Xu 2024 · Xu Shuxi, Peng Houli, Yang Lanxin, et al. · Journal of imaging informatics in medicine · 2024
Included by
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Pooled sensitivity for detection of OIRR AI models (CNN or other deep learning) applied to CBCT for detection and quantification of OIRR vs Manual CBCT interpretation or manual measurement/segmentation (reference standard) | Sensitivity 0.903 [0.818, 0.989] | 3 studies | Moderate |
Pooled sensitivity for detection of OIRR AI models (CNN or other deep learning) applied to CBCT for detection and quantification of OIRR vs Manual CBCT interpretation or manual measurement/segmentation (reference standard) | Sensitivity 0.903 [0.818, 0.989] | 3 studies | GRADE not reported |
Pooled sensitivity for detection of OIRR AI models (CNN or other deep learning) applied to CBCT for detection and quantification of OIRR vs Manual CBCT interpretation or manual measurement/segmentation (reference standard) | Sensitivity 0.93 [0.89, 0.96] | 5 studies · 420 participants | Moderate |
Discriminative accuracy for OIRR detection (AUC) AI models (CNN or other deep learning) applied to CBCT for detection and quantification of OIRR vs Manual CBCT interpretation or manual measurement/segmentation (reference standard) | AUC 0.955 | 2 studies | Low |
1 including review in total.