Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review

作者：

Highlights：

• This study reviews the published studies on the application of machine learning techniques for oral squamous cell carcinoma.

• It examines the concerns and limitations to the actual implementation of machine learning-based models in clinical settings.

• It offers discusses possible solutions to these concerns.

• Support vector machines and artificial neural networks are the most widely used algorithms for oral cancer prognostication.

• Addressing the limitations may ensure that the models are useful for effective oral cancer management.

摘要

•This study reviews the published studies on the application of machine learning techniques for oral squamous cell carcinoma.•It examines the concerns and limitations to the actual implementation of machine learning-based models in clinical settings.•It offers discusses possible solutions to these concerns.•Support vector machines and artificial neural networks are the most widely used algorithms for oral cancer prognostication.•Addressing the limitations may ensure that the models are useful for effective oral cancer management.

论文关键词：Machine learning,Oral squamous cell carcinoma,Systematic review,Explainable AI

论文评审过程：Received 25 May 2020, Revised 27 January 2021, Accepted 23 March 2021, Available online 26 March 2021, Version of Record 8 April 2021.

论文官网地址：https://doi.org/10.1016/j.artmed.2021.102060