Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

TLDR

Periapical radiolucencies are common findings on panoramic radiographs, with differential diagnoses including infections, granulomas, cysts, and tumors. The study investigates the diagnostic accuracy of 24 oral and maxillofacial surgeons in detecting periapical lucencies on panoramic radiographs and compares it to a deep‑learning algorithm. The deep‑learning algorithm was trained on a curated dataset of 2,902 de‑identified panoramic radiographs. The algorithm outperformed 14 of 24 surgeons, achieving an average precision of 0.60 and F1 score of 0.58 (PPV 0.67, TPR 0.51), while surgeons had PPV 0.69 and TPR 0.51, demonstrating the model’s potential to assist detection.

Abstract

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 (±0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 (±0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 (±0.04), and an F1 score of 0.58 (±0.04) corresponding to a PPV of 0.67 (±0.05) and TPR of 0.51 (±0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.