Department of Orthodontics and Pediatric Dentistry, University of Michigan School of Dentistry, Ann Arbor, MI, USA.
Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Orthod Craniofac Res. 2021 Dec;24 Suppl 2(Suppl 2):26-36. doi: 10.1111/ocr.12492. Epub 2021 May 24.
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.
技术和数据收集的进步从各种来源（如健康记录、临床检查、影像学、医疗设备以及实验和生物数据）生成了大量信息。通过高端计算解决方案、人工智能和机器学习方法对这些数据进行适当的管理和分析，可以帮助提取有意义的信息，从而提高人口健康和福祉。此外，提取的知识可以通过临床决策支持系统为现代医疗保健提供新途径。本文对用于正畸学的临床决策支持系统的数据科学方法进行了叙述性综述。我们描述了数据科学方法的基本组成部分，包括（a）数据收集、存储和管理；（b）数据处理；（c）深入数据分析；以及（d）数据通信。然后，我们介绍了一个基于网络的数据管理平台，即用于颞下颌关节和牙科临床决策支持系统的计算和集成数据存储，用于颞下颌关节和牙科临床决策支持系统。
