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CC BY 4.0 · Eur J Dent 2023; 17(04): 1275-1282
DOI: 10.1055/s-0042-1760300
Original Article

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

Paniti Achararit 
1   Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
,
Chawan Manaspon 
2   Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand
,
Chavin Jongwannasiri
1   Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, Thailand
,
Ekarat Phattarataratip 
3   Department of Oral Pathology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
,
Thanaphum Osathanon 
4   Dental Stem Cell Biology Research Unit, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
,
Kraisorn Sappayatosok 
5   College of Dental Medicine, Rangsit University, PathumThani, Thailand
› Author Affiliations Funding/Disclosures This study was approved by the Human Research Ethics Committee, Rangsit University, and was conducted in accordance with the Declaration of Helsinki and adhered to the CONSORT 2010 statement. This study was funded by Rangsit University, Pathum Thani, Thailand.
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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


Publication History

Article published online:
20 January 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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