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Artificial Intelligence in Temporomandibular Joint Imaging: Diagnostic Performance Across Input Modality-Target Pairings-A Systematic Review and Diagnostic Test Accuracy Meta-Analysis

Truvace record
TRV-2026-2861raw data (JSON)
Version
v1Published
First published
Evidence type
Systematic review or meta-analysis
Sources
1 sourcePeer-reviewed
sha256
27a9bfbe6bf0…check it

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…

The gain

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.

What this doesn’t fix

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.

Sources

1 on file
  1. Dentomaxillofacial Radiologydoi:10.1093/dmfr/twag076
    Peer-reviewed2026-11-12

What the review found when it combined the studies

These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.

  • Disc displacement detection by artificial intelligence on panoramic or cone-beam computed tomography: specificity

    Specificity 0.741 (95% CI 0.592 to 0.850)

    search to 18 June 2026

    The review’s own words
    For disc displacement, magnetic resonance imaging input yielded pooled sensitivity 0.924 (95% CI, 0.859-0.960) and specificity 0.812 (0.709-0.885), compared with 0.765 (0.618-0.867) and 0.741 (0.592-0.850) for panoramic or cone-beam computed tomography input.

  • Disc displacement detection by artificial intelligence on panoramic or cone-beam computed tomography: sensitivity

    Sensitivity 0.765 (95% CI 0.618 to 0.867)

    search to 18 June 2026

    The review’s own words
    For disc displacement, magnetic resonance imaging input yielded pooled sensitivity 0.924 (95% CI, 0.859-0.960) and specificity 0.812 (0.709-0.885), compared with 0.765 (0.618-0.867) and 0.741 (0.592-0.850) for panoramic or cone-beam computed tomography input.

  • Disc displacement detection on temporomandibular joint magnetic resonance imaging by artificial intelligence: specificity

    Specificity 0.812 (95% CI 0.709 to 0.885)

    search to 18 June 2026

    The review’s own words
    For disc displacement, magnetic resonance imaging input yielded pooled sensitivity 0.924 (95% CI, 0.859-0.960) and specificity 0.812 (0.709-0.885), compared with 0.765 (0.618-0.867) and 0.741 (0.592-0.850) for panoramic or cone-beam computed tomography input.

  • Disc displacement detection on temporomandibular joint magnetic resonance imaging by artificial intelligence: sensitivity

    Sensitivity 0.924 (95% CI 0.859 to 0.960)

    search to 18 June 2026

    The review’s own words
    For disc displacement, magnetic resonance imaging input yielded pooled sensitivity 0.924 (95% CI, 0.859-0.960) and specificity 0.812 (0.709-0.885), compared with 0.765 (0.618-0.867) and 0.741 (0.592-0.850) for panoramic or cone-beam computed tomography input.

Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results

Cite this record

v1

Truvace Impact Record TRV-2026-2861, v1: “Artificial Intelligence in Temporomandibular Joint Imaging: Diagnostic Performance Across Input Modality-Target Pairings-A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.” Truvace, 2026-11-12. /record/TRV-2026-2861 (accessed on the day you copy it). sha256 27a9bfbe6bf041a8…

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  1. Publishedv127a9bfbe6bf0…

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