evidencegaps
Primary study · included by 1 review

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging.

Kim 2021 · Kim Jae-Young, Kim Dongwook, Jeon Kug Jin, et al. · Scientific reports · 2021

Included by

Diagnostic accuracy of artificial intelligence models for temporomandibular joint anomalies on MRI: a systematic review and meta-analysis.
Non-Cochrane · DTA · 2026 · cited as Kim 2021 · Reference list [19]; Table 1; Figs. 3, 5, 6
Outcome this study informsPooled effectEvidenceCertainty
Specificity
AI (machine/deep learning) models, all architectures pooled vs Reference standard diagnosis of TMJ anomaly
Specificity (pooled proportion) 0.399 [0.343, 0.456]5 studiesGRADE not reported
Sensitivity
AI (machine/deep learning) models, all architectures pooled vs Reference standard diagnosis of TMJ anomaly
Sensitivity (pooled proportion) 0.399 [0.348, 0.450]6 studiesGRADE not reported

1 including review in total.