Primary study · included by 1 review

Development and Validation of a Cbct-Based Artificial Intelligence System for Accurate Diagnoses of Dental Diseases

Ezhov M 2021 · Ezhov Matvey, Gusarev Maxim, Golitsyna Maria, et al. · 2021

doi:10.21203/rs.3.rs-303329/v1posted-content

Trial PICO

Not extracted yet. Trial-level population, intervention, comparator, outcomes and design will appear here after the trial extraction run. Until then, the reviews below describe the study.

Results

Not extracted yet. Per-trial results (events, means, effect and weight as each review's forest plot reports them) will appear here after the trial extraction run.

Included by

Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: An umbrella review with meta-analysis.

Non-Cochrane · DTA · 2025 · cited as Ezhov M 2021 · Table 5 (studies pooled in meta-analysis); Reference [53]

Outcome this study informs	Pooled effect	Evidence	Certainty
Sensitivity AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) · Cross-sectional diagnostic accuracy (index test vs reference standard)	Sensitivity 0.85 [0.83, 0.93]	20 studies · 29423 participants	Ungraded

1 including review in total.