Periapical lesion diagnosis support system based on x-ray images using machine learning technique

Abstract

Aim and objective: The application of artificial intelligence (AI) in diagnosis support is the new approach in telemedicine which is meaningful in disadvantaged areas where are lacking health workers. However, the number of studies investigating the validity of AI in diagnosis support is still few. Periapical lesions, a common dental disease, are conventionally detected by dentists through radiography. This study aimed to assess the application of AI in support the diagnosis of periapical lesions. Materials and methods: One hundred and thirty bite-wing images were recruited to evaluate the sensitivity, specificity, and accuracy of diagnosis provided by DentaVN software. Diagnoses provided by dentists were defined as references. Results: The sensitivity, specificity, and accuracy of diagnoses of the software were 89.5, 97.9, and 95.6%, respectively. Conclusion: DentaVN can be used as a support tool in diagnosis periapical lesions. Clinical significance: To support the diagnosis of periapical diseases in disadvantaged areas where lack of dentists.