Primary study · included by 1 review

Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

Fariza A 2022 · Fariza Arna, Asmara Rengga, Rojaby Muhammad Oktavian Fajar, et al. · 2022 International Conference on Data and Software Engineering (ICoDSE) · 2022

doi:10.1109/icodse56892.2022.9972183proceedings-article

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 Fariza A 2022 · Table 5 (studies pooled in meta-analysis); Reference [61]

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.