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September 10, 2025YARSI Dental JournalOpen Access

Accuracy and Implementation of Dental Clinical Decision Support Systems in Indonesia for Dental Caries and Periodontal Disease

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Authors

AIAwatef IsmailSidra Medical and Research CenterFRFuada Haerana RifaiWestern Regional HospitalAHAinie HasanahWestern Regional Hospital

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Implication

This analysis demonstrates diagnostic accuracy of dental decision support systems for caries and periodontal disease in Indonesia, highlighting user satisfaction and implementation outcomes.

Key Points

  • The study shows that dental CDSS have sensitivity ranging from 81.3% to 96.3% for caries detection, indicating high diagnostic accuracy.
  • Reported specificities for CDSS in detecting caries varied from 92% to 100%, supporting their comparative performance over conventional methods.
  • Assessment methods included advanced techniques like MobileNet-v3 and case-based reasoning to evaluate caries and periodontal disease effectively.
  • Early implementations of CDSS in Indonesia improved user satisfaction, achieving about 88% approval and enhancing early detection of dental issues.

Cite This Study

Ismail et al. (2025) studied this question.

synapsesocial.com/papers/68c183f09b7b07f3a060f9d1https://doi.org/10.33476/ydj.v3i1.321
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Clinical validation of a web-based decision support system for periodontal diagnosis, risk assessment, and recall planning: A multicenter study2026
  2. 2The effectiveness of diagnosing oral mucosal diseases using a clinical decision support system2025 · 1 citations
  3. 3Browser-Based Clinical Decision Support in Indonesian Hospital Workflow: A Multi-Module Implementation Study on ICD-10 Coding Accuracy, Drug Interaction Detection, and Claim Verification2026
  4. 4Accuracy of teledentistry in detecting dental caries among school children in Makkah City: a diagnostic comparative study2026
  5. 5Development and Evaluation of an Expert System for the Early Diagnosis of Dental Diseases2025