Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review

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de Souza, Lucas Lacerda ; Fonseca, Felipe Paiva ; Araujo, Anna Luiza Damaceno ; Lopes, Marcio Ajudarte ; Vargas, Pablo Agustin ; Khurram, Syed Ali ; Kowalski, Luiz Paulo ; dos Santos, Harim Tavares ; Warnakulasuriya, Saman ; Dolezal, James ; Pearson, Alexander. T. T. ; Santos-Silva, Alan Roger

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF ORAL PATHOLOGY & MEDICINE; v. 52, n. 3, p. 9-pg., 2023-03-08.

Resumo

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. (AU)

• Processo FAPESP: 21/14585-7 - Inteligência artificial aplicada ao diagnóstico clínico e histopatológico do Câncer de Cabeça e Pescoço
• Beneficiário: Anna Luiza Damaceno Araujo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 22/03123-5 - Inteligência artificial no diagnóstico de neoplasias de células pequenas, redondas e azuis
• Beneficiário: Lucas Lacerda de Souza
• Modalidade de apoio: Bolsas no Brasil - Doutorado