Primary study · included by 1 review

Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging.

Lee 2022 · Lee Yeon-Hee, Won Jong Hyun, Kim Seunghyeon, et al. · Scientific reports · 2022

Included by

Non-Cochrane · DTA · 2026 · cited as Lee 2022 · Reference list [20]; Table 1; Figs. 3, 5, 6

Outcome this study informs	Pooled effect	Evidence	Certainty
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 studies	GRADE not reported
Accuracy AI (machine/deep learning) models, all architectures pooled vs Reference standard diagnosis of TMJ anomaly	Accuracy (pooled proportion) 0.487 [0.403, 0.571]	3 studies	GRADE 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 studies	GRADE not reported

1 including review in total.