Primary study · included by 2 reviews
Diagnostic accuracy of artificial intelligence-assisted caries detection: a clinical evaluation.
Zhang 2024 · Zhang Jing-Wen, Fan Jie, Zhao Fang-Bing, et al. · BMC oral health · 2024
Included by
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Pooled sensitivity AI (deep learning) models for binary caries classification vs Reference standard (expert examiners or clinical/diagnostic reference) | Sensitivity 0.86 [0.82, 0.89] | 13 studies | GRADE not reported |
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Pooled specificity AI algorithms for dental detection tasks vs Reference standard (expert clinicians) | Specificity (proportion) 0.94 [0.86, 0.97] | 10 studies · 21516 participants | Moderate |
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 |
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 sensitivity AI algorithms for dental detection tasks vs Reference standard (expert clinicians) | Sensitivity (proportion) 0.85 [0.76, 0.91] | 8 studies · 11452 participants | Moderate |
2 including reviews in total.