Automatic detection of spongiosis associated with oral lichenoid lesions using machine learning

Abstract

ObjectiveThe purpose of this study was to evaluate whether it is possible to automatically detect spongiosis on whole-slide images (WSIs) derived from patients with either oral lichenoid lesions (OLLs) or oral lichen planus, aimed at reducing pathologist’s diagnostic time and effort. MethodsPatches from WSIs were used to construct a detection model using the VGG16 convolutional neural network. We conducted a one-step method to directly detect spongiosis and a two-step method to detect spongiosis within the epithelium after detection. To compare the impact of imbalanced data, we performed training with and without data augmentation (DA) and random dawn sampling (RD). ResultsFor spongiosis detection, the two-step model with DA and RD obtained an accuracy of 0.97032, a macro-average F1-score of 0.97020, and an area under the receiver operating characteristic curve (AUC) macro-average receiver operating characteristic (ROC) of 0.99513. For the one-step model with DA and RD, the model obtained an accuracy of 0.97388, a macro-average F1-score of 0.97364, and an AUC macro-average ROC of 0.99738. Compared to the oral pathologists marking of WSIs, we noted that the artificial intelligence (AI) model often over diagnosed. ConclusionsThe constructed machine learning classification model for the detection of spongiosis demonstrated good performance, and it is appropriate that oral pathologists use the AI model to suggest results as a reference to reduce the time and effort required for diagnosis.