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TRUVACE RECORD VERSION
record: TRV-2026-1673
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T06:55:00.753707Z
status: published
lens: p_space
sector: health
headline: AI-Based Clinical Decision Support Systems for Secondary Caries Detection and Staging on Bitewings: A Multi-Algorithm Comparison
dek: Introduction Radiographic detection of caries lesions adjacent to restorations is challenging because of the limitations of two-dimensional imaging and difficulties distinguishing true lesions from restorative or anatomical radiolucencies. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) have been introduced to assist radiographic interpretation; however, different AI tools may yield variable diagnostic outputs, and their comparative performance remains unclear. Objective To compare t…
gain_title: (none)
problem_title: Across eight commercial and experimental AI systems, sensitivity for secondary caries on bitewings was only moderate, so many lesions, especially dentin-stage ones, went undetected.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Across eight commercial and experimental AI systems, sensitivity for secondary caries on bitewings was only moderate, so many lesions, especially dentin-stage ones, went undetected.
problem_evidence: sensitivity was moderate (0.327-0.487), reflecting missed detections of secondary caries lesions, particularly dentin-stage lesions
quick_read: Researchers tested five commercial and three experimental AI systems on 200 anonymized bitewing radiographs containing 885 restored tooth surfaces. The tools were compared against a consensus reference standard for finding and staging caries next to restorations.

The tools performed alike and conservatively. They rarely flagged healthy surfaces but missed many true lesions, which limits their use as a standalone check. The authors suggest more diverse datasets, better reference standards, and greater explainability could improve reliability, though this was not tested here.
limitation: The study found that missed lesions were common, and that errors were tied to overlap, restoration-related radiolucencies and cervical artifacts. The authors say better dataset diversity and reference-standard quality are still needed.
tag: Evidence-backed problem
key_points: The cross-sectional study used 200 bitewings covering 885 restored tooth surfaces. | Specificity ranged from 0.957 to 0.986 across all systems. | Sensitivity ranged only from 0.327 to 0.487, with most misses in dentin-stage lesions. | Accuracy of 0.882 to 0.917 did not differ significantly among systems (p ≥ 0.05).
rundown: The systems evaluated were Second Opinion, CranioCatch, Diagnocat, DIO Inteligência and Align X-ray Insights, plus three experimental models built on Mask R-CNN and Mask DINO architectures. Each restored surface was classified as non-carious, secondary caries, or indeterminate, and lesions were staged as enamel or dentin.

Performance was measured with sensitivity, specificity, accuracy, and positive and negative predictive values with 95% confidence intervals. Generalized estimating equations adjusted for clustering of surfaces within images.

Errors between non-carious and carious surfaces were often linked to radiographic overlap, restoration-related radiolucencies, and cervical artifacts. The authors conclude that architecture and commercial status made little difference to diagnostic capability.
sources:
- peer_reviewed | Caries Research | https://doi.org/10.1159/cre/abuag016 | 2026-10-09
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