Impact of Machine Learning and Prediction Models in the Diagnosis of Oral Health Conditions

Abstract

Introduction: Recent developments in data science and the employment of machine learning algorithms (ML) have revolutionized health sciences in the prediction of diseases using laboratory data. Oral diseases are observed in all age groups and are estimated to affect about a 3.5billion people as per WHO 2022 statistics. Using the existing diagnostic data and taking advantage of ML and prediction models would benefit developing a prediction model for diagnosing oral diseases. Hence, it is quite essential to understand the basic terminologies used in the prediction model. Methods: We retrieve various research papers using Scopus, PubMed, and google scholar databases, where prediction models were used in dentistry. The idea of this review is to explore current models, model validation, discrimination, calibration, and bootstrapping methods used in prediction models for oral diseases. Results: The current advancement of ML techniques plays a significant task in the diagnosis and prognosis of oral diseases. Conclusion: The use of prediction models using ML techniques can improve the accuracy of the treatment methods in oral health. This article aims to provide the required framework, data sets, and methodology to build ML and prediction models for oral diseases.