Machine-Learning Applications in Oral Cancer: A Systematic Review
Source abstract
Abstract
Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Oral Health Pathology and Treatment · Head and Neck Cancer Studies · AI in cancer detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca 3480112, Chile
Department of Stomatology, Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile
Department of Basic Biomedical Sciences, Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile
Funding
Program of International Cooperation from the Agencia Nacional de Investigación y Desarrollo (ANID)
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