A state-of-the-art approach for dental diagnosis using machine learning assessment: A short communication

Abstract

In the modern era, intelligent systems are playing a vital role in day-to-day human life-related applications. It is not only giving solutions to handling health-care applications for dental treatments. In this context, artificial intelligence-based machine learning (ML) algorithms are used for experimenting with the trained data sets that progress the relevances in decision-making. A specific practice The “Internet of Dental Things” is a ground-breaking method to accomplish the avoidance and supervision of periodontal diseases, dental caries, oral cancers, and other dental diseases. Various ML algorithms such as convolutional neural network, K-nearest neighbor, and random Forest are used for anticipating nodal status and tumor grade in oropharyngeal and oral cavity squamous cell carcinoma. The ML algorithms help with diagnosis, assistive systems, and monitoring toward decision-making and contribute to the pursuit of suggesting an exact dental treatment. It is helpful in diagnosing tooth loss, cracked teeth, periodontitis, dental fluorosis, dental caries, dental calculus, dental plaque, and periodontal disease. The suggestions given by the intelligent system render accuracy, precision, specificity, sensitivity, confusion matrix, recall, and F1-Score to strengthen the decision-making and contribute to the pursuit of offering an exact treatment.