Abstract Purpose Oral squamous cell carcinoma (OSCC) is the most common oral cancer, with high mortality rates, making early detection crucial. Malignant transformation is preceded by oral potentially malignant disorders (OPMDs), with leukoplakia being the most frequent. Epithelial dysplasia (ED) is a key histological feature for predicting the risk of progression to OSCC. However, its diagnosis relies on subjective visual assessment by pathologists, which is influenced by experience and emotional state. This study aims to develop a Machine Learning (ML), based methodology to assist in ED detection in leukoplakia lesions. Methods The proposed methodology integrates histopathological image analysis with complementary patient data. Epithelial regions were selected for cutout extraction based on pathologist knowledge and combined with risk factors from patient data. A multilayer perceptron artificial neural network (MLP-ANN) was trained and evaluated. Performance was compared with a pre-trained convolutional neural network (ResNet-50V2) using the McNemar test. Results The MLP-ANN achieved an area under the curve (AUC) of 0.9484. No statistically significant difference was found between the proposed method and the pre-trained CNN. However, the proposed approach was approximately 9.7 times faster in computational time. Conclusions The proposed ML-based methodology demonstrated competitive classification performance along with improved computational efficiency. Refining the confidence criterion for intermediate classification results may further enhance its diagnostic accuracy, supporting pathologists in ED detection.
Rocha et al. (Mon,) studied this question.