Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning.

Abstract

Periapical index (PAI) scoring system is the most popular index for evaluating apical periodontitis (AP) on radiographs. It provides an ordinal scale of 1 to 5, ranging from healthy to severe AP. Scoring PAI is a time-consuming process and requires experienced dentists; thus, deep learning has been applied to hasten the process. However, most models failed to score the early stage of AP or the score 2 accurately since it shares very similar characteristics with its adjacent scores. In this study, we developed and compared binary classification methods for PAI scores which were normality classification method and health-disease classification method. The normality classification method classified PAI score 1 as Normal and Abnormal for the rest of the scores while the health-disease method classified PAI scores 1 and 2 as Healthy and Diseased for the rest of the scores. A total of 2266 periapical root areas (PRAs) from 520 periapical radiographs (Pas) were selected and scored by experts. GoogLeNet, AlexNet, and ResNet convolutional neural networks (CNNs) were used in this study. Trained models' performances were evaluated and then compared. The models in the normality classification method achieved the highest accuracy of 75.00%, while the health-disease method models performed better with the highest accuracy of 83.33%. In conclusion, CNN models performed better in classification when grouping PAI scores 1 and 2 together in the same class, supporting the health-disease PAI scoring usage in clinical practice.