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. 2023 Oct;17(4):1275-1282.
doi: 10.1055/s-0042-1760300. Epub 2023 Jan 20.

Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

Paniti Achararit  1 , Chawan Manaspon  2 , Chavin Jongwannasiri  1 , Ekarat Phattarataratip  3 , Thanaphum Osathanon  4 , Kraisorn Sappayatosok  5
Affiliations

Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

Paniti Achararit et al. Eur J Dent. 2023 Oct.

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.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Framework of the method for oral lichen planus (OLP) and non-OLP lesion diagnosis using convolutional neural network.
Fig. 2
Fig. 2
Metrics of performance for CNN models in OLP and non-OLP diagnosis. CNN, convolutional neural network; FN, false negative; FP, false positive; NPV, negative predictive value; OLP, oral lichen planus; PPV, positive predictive value; TN, true negative; TP, true positive.
Fig. 3
Fig. 3
Gradient-weighted class activation mapping visualization of convolutional neural network classification for oral lichen planus (OLP) and non-OLP lesions (traumatic ulcer) from Xception, ResNet152V2, and EfficientNetB3 models.
Fig. 4
Fig. 4
Misclassification photographs for Xception (hyperkeratosis), ResNet152V2 (traumatic ulcer), and EfficientNetB3 models (lupus erythematosus). OLP, oral lichen planus.
See this image and copyright information in PMC

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