Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, 10021, USA; Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, 43180, Sweden.
Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, 03080, South Korea; CONNECTEVE Co., Ltd, Seoul, 03080, South Korea.
J ISAKOS. 2024 Aug;9(4):635-644. doi: 10.1016/j.jisako.2024.01.013. Epub 2024 Feb 7.
Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty.
机器学习（ML）正在改变医疗保健的实践方式，这些新颖的统计技术的最新应用已经开始对矫形运动医学产生影响。机器学习使我们能够分析大量数据，以建立“输入”和“输出”变量之间的复杂关系。这些关系可能比传统的统计分析更为复杂，并且可以实现高精度预测“输出”的能力。监督学习是医疗保健数据中最常见的 ML 方法，最近的研究已经开发出算法来预测髋关节镜检查和前交叉韧带重建等手术程序后的患者特定结果。深度学习是一种更高级的 ML 方法，它通过受人类大脑处理信息方式启发的人工神经网络来促进复杂数据集的处理和解释。在矫形运动医学中，深度学习主要用于自动图像（计算机视觉）和文本（自然语言处理）解释。虽然矫形运动医学中的应用呈指数级增长，但 ML 广泛采用的一个重大障碍仍然是临床医生对相关方法和概念不熟悉。本综述的目的是介绍这些概念，回顾矫形运动医学中的当前机器学习模型，并讨论该专业内的创新机会。
