Primary study · included by 2 reviews

Diagnosis of Tempromandibular Disorders Using Local Binary Patterns.

Haghnegahdar et al. (2018) · Haghnegahdar A A, Kolahi S, Khojastepour L, et al. · Journal of biomedical physics & engineering · 2018

Included by

Non-Cochrane · DTA · 2026 · cited as Haghnegahdar 2018 · Table 1; Figure 4

Outcome this study informs	Pooled effect	Evidence	Certainty
Pooled sensitivity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus)	Sensitivity 0.751 [0.703, 0.794]	5 studies · 866 participants	GRADE not reported
Pooled sensitivity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus)	Change in pooled sensitivity (leave-one-out delta) -0.014	4 studies	GRADE not reported

Non-Cochrane · DTA · 2026 · cited as Haghnegahdar 2018 · Table 3; reference [18]

Also linked to PM42587704-P2-O1, which does not match a current question in this review.

2 including reviews in total.