Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review
Lucas Lacerda de Souza, Felipe Paiva Fonseca, Anna Luíza Damaceno Araújo, Márcio Ajudarte Lopes, Pablo Agustín Vargas, Syed Ali Khurram, Luiz Paulo Kowalski, Harim Tavares dos Santos, Saman Warnakulasuriya, James M. Dolezal, Alexander T. Pearson, Alan Roger Santos‐Silva
Abstract
Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.