Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual or clinical examination: Specificity
Across 13 studies, the specificity of teledentistry caries diagnosis ranged from 83% to 100%; no pooled estimate and certainty not graded.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Specificity · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), In-vivo: dental clinics/hospitals, schools, and a juvenile detention facility (11 countries), Overall (studies reporting specificity; narrative synthesis) Across 13 studies, the specificity of teledentistry caries diagnosis ranged from 83% to 100%; no pooled estimate and certainty not graded. Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) All outcomes | Specificity ranged from 83% to 100% across studies (reported in 13 of 19 studies). No pooled estimate; meta-analysis not performed. | 13 studies | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral photography methods. 2024 All outcomes | February 2023 (electronic searches conducted February 2023; literature coverage through December 2022; no date restriction applied) | 19 | 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.