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TRUVACE RECORD VERSION
record: TRV-2026-2861
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T18:02:00.376957Z
status: published
lens: g_space
sector: health
headline: Artificial Intelligence in Temporomandibular Joint Imaging: Diagnostic Performance Across Input Modality-Target Pairings-A Systematic Review and Diagnostic Test Accuracy Meta-Analysis
dek: To estimate standalone artificial intelligence (AI) diagnostic performance in temporomandibular joint (TMJ) imaging across input modality-target pairings. Four databases were searched through 18 June 2026. Eligible studies assessed standalone imaging AI and supplied an exact test-set 2 × 2 table in the report or after author contact. Four pairings were analysed by bivariate meta-regression. Two reviewers applied QUADAS-3 at estimate level. Sixteen studies contributed 17 estimates. For disc displacement, magnetic…
gain_title: A systematic review of 16 studies found that AI reading MRI scans for temporomandibular joint disc displacement reached pooled sensitivity of 0.924 and specificity of 0.812.
problem_title: (none)
trace_subject: (none)
gain_reading: A systematic review of 16 studies found that AI reading MRI scans for temporomandibular joint disc displacement reached pooled sensitivity of 0.924 and specificity of 0.812.
gain_evidence: magnetic resonance imaging input yielded pooled sensitivity 0.924 (95% CI, 0.859-0.960) and specificity 0.812 (0.709-0.885)
problem_reading: (none)
problem_evidence: (none)
quick_read: A systematic review and meta-analysis pooled 16 studies (17 estimates) on standalone AI diagnosis of temporomandibular joint disorders from imaging. For disc displacement, AI reading MRI reached pooled sensitivity of 0.924 and specificity of 0.812.

With panoramic or cone-beam CT input, where the disc is not directly visible, pooled sensitivity was 0.765 and specificity 0.741. The sensitivity gap was 0.159, and it stayed between 0.152 and 0.198 in leave-one-out and replicate analyses.

The authors say performance should be read by input-and-target pairing, not as a fixed trait of the algorithm. The formal test across pairings was not significant, and none of the estimates had low overall risk of bias, so the MRI advantage needs prospective testing.
limitation: The cross-domain group contained only three estimates, the omnibus pairing test was not significant, design factors were entangled, and no estimate had low overall risk of bias.
tag: Evidence-backed gain
key_points: Four databases were searched through 18 June 2026; studies had to assess standalone imaging AI and supply an exact test-set 2 × 2 table, either published or obtained from the authors. | Sixteen studies contributed 17 estimates, analysed with bivariate meta-regression across four input-target pairings. | For MRI input, pooled sensitivity was 0.924 (95% CI 0.859-0.960) and specificity 0.812 (0.709-0.885). | For panoramic or cone-beam CT input, pooled sensitivity was 0.765 (0.618-0.867) and specificity 0.741 (0.592-0.850). | The sensitivity difference was 0.159, and 0.152-0.198 in leave-one-out and replicate analyses. | The omnibus pairing test was not significant (χ²(6) = 10.38, P = .110). | No estimate had low overall risk of bias under QUADAS-3, and several otherwise eligible estimates lacked exact published 2 × 2 data.
rundown: Two reviewers rated risk of bias with QUADAS-3 at the level of each estimate. Only studies reporting an exact test-set 2 × 2 table, in the paper or after contacting authors, were included, so accuracy could be verified.

The review separated disc displacement read directly on MRI from displacement inferred from panoramic or cone-beam CT images that do not depict the disc. Sensitivity was higher in the direct-depiction group.

The reference standard used and the input pairing split the disc-displacement evidence identically, so the authors say design factors were entangled with imaging type.
sources:
- peer_reviewed | Dentomaxillofacial Radiology | https://doi.org/10.1093/dmfr/twag076 | 2026-11-12
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