Question · asked by 1 review
Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction: Molar caries detection accuracy
- Population
- Dental images of human beings
- Intervention
- Machine-learning algorithms for dental caries detection
- Comparison
- Different machine-learning algorithms for caries prediction
- Outcome
- Molar caries detection accuracy
The review reported 89% accuracy for molar caries in Lee et al. (Ungraded).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction Molar caries detection accuracy · Dental images of human beings · Korea, Lee et al., 2018; CNN The review reported 89% accuracy for molar caries in Lee et al. (Ungraded). Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. (2022) All outcomes | Accuracy 89 | 1 study · 3000 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. 2022 All outcomes | Not specified | 17 | Not applicable |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.