| Title: | Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis |
| Author(s): | Ferro A; Kotecha S; Fan K; |
| Address: | "Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK. Oral and Maxillofacial Surgery Department, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE1 9RT, UK. Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK. kfan@nhs.net. Oral and Maxillofacial Surgery Department, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE1 9RT, UK. kfan@nhs.net" |
| DOI: | 10.1038/s41598-022-17489-1 |
| ISSN/ISBN: | 2045-2322 (Electronic) 2045-2322 (Linking) |
| Abstract: | "Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this systematic review and meta-analysis, we provide an update on current progress of ML algorithms in point-of-care (POC) automated diagnostic classification systems for lesions of the oral cavity. Studies reporting performance metrics on ML algorithms used in automatic classification of oral regions of interest were identified and screened by 2 independent reviewers from 4 databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 35 studies were suitable for qualitative synthesis, and 31 for quantitative analysis. Outcomes were assessed using a bivariate random-effects model following an assessment of bias and heterogeneity. 4 distinct methodologies were identified for POC diagnosis: (1) clinical photography; (2) optical imaging; (3) thermal imaging; (4) analysis of volatile organic compounds. Estimated AUROC across all studies was 0.935, and no difference in performance was identified between methodologies. We discuss the various classical and modern approaches to ML employed within identified studies, and highlight issues that will need to be addressed for implementation of automated classification systems in screening and early detection" |
| Keywords: | Algorithms Diagnostic Imaging *Machine Learning Mass Screening *Point-of-Care Systems; |
| Notes: | "MedlineFerro, Ashley Kotecha, Sanjeev Fan, Kathleen eng Meta-Analysis Research Support, Non-U.S. Gov't Systematic Review England 2022/08/14 Sci Rep. 2022 Aug 13; 12(1):13797. doi: 10.1038/s41598-022-17489-1" |
