Primary study · included by 1 review
Correction: YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
Beser 2024 · Beser Busra, Reis Tugba, Berber Merve Nur, et al. · BMC medical imaging · 2024
Included by
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Risk difference for precision in segmentation tasks AI segmentation algorithms (e.g. U-Net, YOLO, SAM) vs Expert annotation (reference standard) | RD 0.98 [0.97, 0.98] | 2 studies · 4039 participants | GRADE not reported |
Pooled precision AI algorithms (deep learning, mainly CNN/YOLO) for dental detection tasks vs Reference standard (expert clinicians or consensus) | Precision 0.88 [0.71, 0.96] | 7 studies · 23248 participants | Moderate |
Precision in segmentation tasks (risk difference) AI algorithms for dental segmentation tasks vs Expert annotation (reference standard) | RD 0.86 [-0.73, 2.46] | 2 studies | GRADE not reported |
Pooled F1 score AI algorithms (deep learning, mainly CNN/YOLO) for dental detection tasks vs Reference standard (expert clinicians or consensus) | F1 score 0.90 [0.77, 0.96] | 8 studies · 24033 participants | Moderate |
Pooled Dice similarity coefficient (DSC/F1) AI segmentation algorithms (e.g. U-Net, YOLO, SAM) vs Expert annotation (reference standard) | Dice similarity coefficient 0.89 [0.13, 1.00] | 3 studies · 4539 participants | Low |
1 including review in total.