Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction: Caries detection accuracy in unrestored molars and premolars
The review reported 92.37% accuracy for automated caries detection in Duong et al. (Ungraded).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction Caries detection accuracy in unrestored molars and premolars · Dental images of human beings · Taiwan, Duong et al., 2021; support vector machine The review reported 92.37% accuracy for automated caries detection in Duong et al. (Ungraded). Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. (2022) All outcomes | Accuracy 92.37 | 1 study · 620 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 |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.