Abstract
Objective The aim of this study was to employ artificial intelligence (AI) via convolutional
neural network (CNN) for the separation of oral lichen planus (OLP) and non-OLP in
biopsy-proven clinical cases of OLP and non-OLP.
Materials and Methods Data comprised of clinical photographs of 609 OLP and 480 non-OLP which diagnosis
has been confirmed histopathologically. Fifty-five photographs from the OLP and non-OLP
groups were randomly selected for use as the test dataset, while the remaining were
used as training and validation datasets. Data augmentation was performed on the training
dataset to increase the number and variation of photographs. Performance metrics for
the CNN model performance included accuracy, positive predictive value, negative predictive
value, sensitivity, specificity, and F1-score. Gradient-weighted class activation
mapping was also used to visualize the important regions associated with discriminative
clinical features on which the model relies.
Results All the selected CNN models were able to diagnose OLP and non-OLP lesions using photographs.
The performance of the Xception model was significantly higher than that of the other
models in terms of overall accuracy and F1-score.
Conclusions Our demonstration shows that CNN models can achieve an accuracy of 82 to 88%. Xception
model performed the best in terms of both accuracy and F1-score.
Keywords
oral lichen planus - convolution neural network - AI-based diagnosis - oral lesions
- artificial intelligence
