Comparative Performance of AI-Based Algorithms for Caries Detection in Dentistry (2025)
- Authors:
- Autor USP: MENDES, FAUSTO MEDEIROS - FO
- Unidade: FO
- Subjects: INTELIGÊNCIA ARTIFICIAL; CÁRIE DENTÁRIA
- Language: Português
- Abstract: This study compared the diagnostic performance of commercial (Second Opinion®, Diagnocat®, CranioCatch®) and experimental models, for the detection of caries lesions in bitewings. Research-based models consisted in mask architectures, independently trained with different datasets, developed at the Radboud AI Lab. Occlusal and proximal surfaces of set of 200 bitewings were manually annotated by a cariology expert, forming the referential for comparisons. Accuracy in detecting Global Caries (GC), Secondary Caries (SC), and Dentin Lesions-only (DL) were calculated per surface. Second Opinion® was consistently the highest sensitivity across all analyses (GC: 93.0%; SC: 83.5%; DL: 89.6%), with corresponding AUCs up to 0.91. In contrast, Diagnocat® presented a more balanced profile, combining moderate-to-high sensitivity with strong specificity (SC: 95.1%; DL: 97.9%; AUCs 0.78-0.87). CranioCatch® achieved the highest specificity among the commercial models-especially for DL (98.5%)-but this came at the expense of sensitivity, which remained below 53%. The experimental models were optimized to reduce false positives, maintaining specificities above 97% for SC and up to 98.7% for DL, though with more modest sensitivities (65-75%) and AUCs between 0.75 and 0.85. While lower sensitivity may lead to missed early-stage lesions, these are often slow-progressing and clinically manageable without immediate intervention. In contrast, false positives can prompt unnecessary restorative procedures and the irreversible removal of healthy tissue. These findings highlight the importance of model selection based on clinical context and support the integration of AI with professional judgment in caries detection
- Imprenta:
- Source:
- Título: Brazilian Oral Research
- ISSN: 1807-3107
- Volume/Número/Paginação/Ano: v. 39, supl. 2, res. HA003, 2025
- Conference titles: Proceedings of the SBPqO Annual Meeting
-
ABNT
CHAVES, E. T et al. Comparative Performance of AI-Based Algorithms for Caries Detection in Dentistry. Brazilian Oral Research. São Paulo: SBPqO. . Acesso em: 17 fev. 2026. , 2025 -
APA
Chaves, E. T., Romero, V. H. D., Nistelrooij, N., Vinayahalingam, S., Mendes, F. M., Cenci, M. S., et al. (2025). Comparative Performance of AI-Based Algorithms for Caries Detection in Dentistry. Brazilian Oral Research. São Paulo: SBPqO. -
NLM
Chaves ET, Romero VHD, Nistelrooij N, Vinayahalingam S, Mendes FM, Cenci MS, Huysmans MD, Lima GS. Comparative Performance of AI-Based Algorithms for Caries Detection in Dentistry. Brazilian Oral Research. 2025 ; 39[citado 2026 fev. 17 ] -
Vancouver
Chaves ET, Romero VHD, Nistelrooij N, Vinayahalingam S, Mendes FM, Cenci MS, Huysmans MD, Lima GS. Comparative Performance of AI-Based Algorithms for Caries Detection in Dentistry. Brazilian Oral Research. 2025 ; 39[citado 2026 fev. 17 ] - Exploring the association between genetic and environmental factors and molar incisor hypomineralization: evidence from a twin study
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