aceAI-Based Clinical Decision Support Systems for Secondary Caries Detection and Staging on Bitewings: A Multi-Algorithm Comparison
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…
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.
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.
Evidence
- Peer-reviewedCaries Research2026-10-09
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Truvace Impact Record TRV-2026-1673, v1: “AI-Based Clinical Decision Support Systems for Secondary Caries Detection and Staging on Bitewings: A Multi-Algorithm Comparison.” Truvace, 2026-10-17. /record/TRV-2026-1673 (accessed at citation time). sha256 8766fd3a19ed223e…
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