Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications

• Published in: Seminars in orthodontics Vol. 27; no. 2; pp. 78 - 86
• Main Authors: Bianchi, Jonas, Ruellas, Antonio, Prieto, Juan Carlos, Li, Tengfei, Soroushmehr, Reza, Najarian, Kayvan, Gryak, Jonathan, Deleat-Besson, Romain, Le, Celia, Yatabe, Marilia, Gurgel, Marcela, Turkestani, Najla Al, Paniagua, Beatriz, Cevidanes, Lucia
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.06.2021
• ISSN: 1073-8746; 1558-4631
• DOI: 10.1053/j.sodo.2021.05.004

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.