Accuracy of Al Algorithms in Diagnosing Periodontal Disease Using Intraoral Images

Abstract

ABSTRACT Background: Periodontal disease, characterized by inflammation and damage to tooth-supporting structures, poses a prevalent oral health concern. Early detection is crucial for effective management. Materials and Methods: This study comprised of 60 patients with varying degrees of periodontal disease. Intraoral images were captured using digital cameras, and AI algorithms were trained to analyze these images for signs of periodontal disease. Clinical diagnoses, conducted by experienced periodontal specialists, were used as the reference standard. Results: The AI algorithms achieved an overall accuracy of 87% in diagnosing periodontal disease. Sensitivity was 90%, indicating the AI’s ability to correctly identify 90% of true cases, while specificity stood at 84%, demonstrating its capability to accurately classify 84% of non-diseased cases. In comparison, clinical diagnosis yielded an overall accuracy of 86%. Statistical analysis showed no significant difference between AI-based diagnosis and clinical examination (P > 0.05). Conclusion: This study underscores the promising potential of AI algorithms in diagnosing periodontal disease through intraoral image analysis.