Radiomics and Machine Learning in Oral Healthcare

Proteomics Clin Appl. 2020 May;14(3):e1900040. doi: 10.1002/prca.201900040. Epub 2020 Jan 29.


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.

Keywords: CBCT; machine learning; oral health; radiomics.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Delivery of Health Care / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning*
  • Oral Health*