Use and performance of artificial intelligence applications in the diagnosis of chronic apical periodontitis based on cone beam computed tomography imaging.

OBJECTIVES

This study aims to investigate the diagnostic application of an artificial intelligence (AI) computer-aided diagnostic system based on a convolutional neural network algorithm in detecting chronic apical periodontitis in cone beam computed tomography (CBCT) images.

METHODS

CBCT raw data of 55 single root chronic apical pe-riodontitis taken in 2nd Dental Center of Peking University School and Hospital from 49 patients from January 2017 to December 2021 were collected, and the chronic apical periodontitis areas were identified by experienced clinicians ma-nually and segmented layer by layer in Materialise Mimics Medical Software. Deep learning of lesion characterization was conducted via AI 3D U-Net, and the network segmentation results were compared manually with the test sets in terms of intersection over union (IOU), Dice coefficient, and pixel accuracy (PA).

RESULTS

In our deep learning algorithm, the IOU for all actual true lesions in test set samples was 92.18%, and the Dice coefficient and the PA index were 95.93% and 99.27%, respectively. Lesion segmentation and volume measurements performed by humans and AI systems showed excellent agreement.

CONCLUSIONS

AI systems based on deep learning methods can be applied for detecting chronic apical periodontitis on CBCT images in clinical applications.