Citation: López-Cortés, X.A.; Matamala, F.; Venegas, B.; Rivera, C. Machine-Learning Applications in Oral Cancer: A Systematic Review. Appl. Sci. 2022, 12, 5715. https:// doi.org/10.3390/app12115715 Academic Editors: Alberta Lucchese, Dario Di Stasio and Gianrico Spagnuolo Received: 7 December 2021 Accepted: 31 May 2022 Published: 4 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). applied sciences Review Machine-Learning Applications in Oral Cancer: A Systematic Review Xaviera A. López-Cortés 1, * , Felipe Matamala 1 , Bernardo Venegas 2 and César Rivera 3 1 Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca 3480112, Chile; pipexmatamala@gmail.com 2 Department of Stomatology, Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; bvenegas@utalca.cl 3 Department of Basic Biomedical Sciences, Faculty of Health Sciences, Universidad de Talca, Talca 3460000, Chile; cerivera@utalca.cl * Correspondence: xlopez@ucm.cl 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. Keywords: oral cancer; OSCC; machine learning; applications 1. Introduction Oral cancer has emerged as a serious public health issue across the world. According to the literature, the global incidence, mortality, and disability-adjusted life years of this disease increased by nearly 1.0-fold between 1990 and 2017 [1]. Based on the GLOBOCAN estimates of incidence and mortality, 377,713 new cases and 177,757 deaths for lip and oral cavity cancer were reported for the year 2020 [2]. Most oral cancers are squamous cell carcinomas, which is an aggressive disease with a high tendency to metastasize locally and to distant sites. It has a considerable impact on a patient’s life and on society as a whole. Oral cancer has a 5-year overall survival rate of just approximately 51.7 percent due to frequently late diagnosis [3]. The methods used for oral cancer diagnosis include the traditional anamnesis and clinical examination, complemented with image and hematoxylin–eosin histopathologi- cal analysis, the latter being the most common method [4,5]. Immunohistochemistry is routinely used to distinguish the disease in more complex instances and to aid in disease staging. For its examination, molecular approaches have been devised with the goal of finding biomarkers that can anticipate early alterations. In situ hybridization, gel elec- trophoresis and blotting, flow cytometry, mass spectrometry, polymerase chain reaction, microarrays, Sanger sequencing, and next-generation sequencing are common techniques employed in molecular diagnostics of oral squamous cell carcinomas [6]. In the practice of clinical medicine and in all health-related tasks, the diagnostic process is critical. A correct diagnostic evaluation is essential for the effectiveness of disease therapy. This diagnostic process is based on the interpretation of information supplied by the patient in the anamnesis, as well as the clinician’s clinical examination, in addition to Appl. Sci. 2022, 12, 5715. https://doi.org/10.3390/app12115715 https://www.mdpi.com/journal/applsci