evidence/gaps
Primary study · included by 2 reviews

Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography.

De Araujo Faria V 2021 · De Araujo Faria Vanessa, Azimbagirad Mehran, Viani Arruda Gustavo, et al. · Journal of digital imaging · 2021

PubMed 34254199doi:10.1007/s10278-021-00487-6Journal Article; Research Support, Non-U.S. Gov't

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

Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis.
Non-Cochrane · DTA · 2025 · cited as De Araujo Faria 2021 · Table 2; reference 22

The review does not say which of its outcomes this study informs.

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 De Araujo Faria V 2021 · Table 5 (studies pooled in meta-analysis); Reference [56]
Outcome this study informsPooled effectEvidenceCertainty
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 participantsUngraded

2 including reviews in total.