Deep learning-based efficient diagnosis of periapical diseases with dental X-rays

Abstract

Diagnosis of periapical diseases usually requires a lot of time and effort from the dentist to perform a series of examinations and evaluations, although X-rays have been available to assist in visualizing the condition of the apical region of the tooth. To achieve efficient automated detection and diagnosis of periapical diseases (periapical granuloma, periapical abscess, periapical cysts, and condensing osteitis) by dental X-rays, a deep learning-based approach is proposed, which includes an improved Mask R-CNN-based lesion area segmentation network and a neural network classifier based on six texture features extracted from the segmented lesion regions. The results demonstrate that the average pixel accuracy of lesion area segmentation is over 0.97, while the average Dice Similarity Coefficient for segmentation is over 0.95. The accuracy, precision, recall, and F1 score of this method for the diagnosis of periapical diseases are all above 0.99. Precise segmentation and accurate classification of four distinct types of periapical diseases are achieved using this method, making it possible to create automated systems that can assist in the clinical diagnosis of periapical diseases, potentially streamlining the diagnostic process, reducing the workload of dentists, and improving overall patient care.