Department of Orthodontics, University of Illinois at Chicago College of Dentistry, Chicago, Illinois.
Department of Orthodontics and Dentofacial Orthopedics, University of Missouri at Kansas City School of Dentistry, Kansas City, Missouri.
Orthod Craniofac Res. 2019 May;22 Suppl 1:8-13. doi: 10.1111/ocr.12279.
The objective of this report was to provide an overview of the current landscape of big data analytics in the healthcare sector, introduce various approaches of machine learning and discuss potential implications in the field of orthodontics. With the increasing availability of data from various sources, the traditional analytical methods may not be conducive anymore for examining clinical outcomes. Machine-learning approaches, which are algorithms trained to identify patterns in large data sets, are ideally suited to facilitate data-driven decision making. The field of orthodontics is particularly ripe for embracing the big data analytics platform to improve decision making in clinical practice. The availability of omics data, state-of-the-art imaging and potential for establishing large clinical data repositories have favourably positioned the specialty of orthodontics to deliver personalized and precision orthodontic care. Specifically, we discuss about next-generation sequencing, radiomics in the context of CBCT imaging, and how centralized data repositories can enable real-time data pooling from multiple sources.
本报告旨在概述医疗保健领域大数据分析的现状，介绍机器学习的各种方法，并讨论在正畸领域的潜在影响。随着来自各种来源的数据越来越多，传统的分析方法可能不再适用于检查临床结果。机器学习方法是经过训练以识别大数据集中模式的算法，非常适合促进数据驱动的决策。正畸领域特别适合采用大数据分析平台来改善临床实践中的决策。组学数据、最先进的成像技术以及建立大型临床数据存储库的潜力，使正畸专业能够提供个性化和精准的正畸护理。具体来说，我们讨论了下一代测序、CBCT 成像中的放射组学，以及集中式数据存储库如何能够实现来自多个来源的实时数据汇总。
