Radiomics and Machine Learning in Oral Healthcare

• Published in: Proteomics. Clinical applications Vol. 14; no. 3; pp. e1900040 - n/a
• Main Authors: Leite, André Ferreira, Vasconcelos, Karla de Faria, Willems, Holger, Jacobs, Reinhilde
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.05.2020
• Subjects: Artificial intelligence; CBCT; Computer applications; Delivery of Health Care - methods; Dentistry; Evaluation; Health care; Human error; Humans; Image Processing, Computer-Assisted - methods; Information storage; Learning algorithms; Machine Learning; New technology; Oral diseases; Oral Health; Radiology; Radiomics; Task complexity; radiomics; oral health; machine learning; CBCT
• ISSN: 1862-8346; 1862-8354; 1862-8354
• DOI: 10.1002/prca.201900040

The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state‐of‐the‐art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics‐based and AI‐based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.