Clinical decision support systems in orthodontics: A narrative review of data science approaches
Najla Al Turkestani, Jonas Bianchi, Romain Deleat‐Besson, Celia Le, Tengfei Li, Juan Carlos Prieto, Marcela Gurgel, A.C.O. Ruellas, Camila Massaro, Arón Aliaga-Del Castillo, Karine Evangelista, Marília Yatabe, Erika Benavides, Fabiana N. Soki, Winston Zhang, Kayvan Najarian, Jonathan Gryak, Martin Styner, Jean‐Christophe Fillion‐Robin, Beatriz Paniagua, S. M. Reza Soroushmehr, Lucía Cevidanes
Abstract
Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.