Primary study · included by 1 review
De Araujo Faria 2021
ProbableThe reviews cited this study without a PMID, DOI or NCT, so it is matched by first author and year. Two different studies by the same author in the same year could be merged here.
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
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~80 | 1 study | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~76 | NR | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | ~70 | 1 study | Ungraded |
Diagnostic accuracy (prevalence of accuracy) AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) | OR 2.72 [14.65, 14.65] | 14 studies | Ungraded |
1 including review in total.