Dataset for "Machine learning outperforms clinical experts in classification of hip fractures"

Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%. This data set contains the source data for figures 2 and 4, which are the main Results figures. Data are given in both csv and MAT file formats. The MATLAB scripts for generating the figures are also provided.

髋部骨折是老年人群发病与死亡的主要诱因之一，同时会产生高昂的医疗与社会照护成本。鉴于全球人口老龄化的发展趋势，新发髋部骨折病例数预计将持续攀升。由于骨折分型是外科手术治疗方案选择的核心依据，骨折分型的差异会直接影响患者预后与治疗成本。本研究旨在构建一种用于髋部骨折识别与分型的机器学习方法，并将其性能与资深临床观察者进行对比。本研究共纳入3659张髋部X线片，所有影像均经至少两名资深临床医师完成分型标注。该机器学习方法的髋部骨折分型准确率较人类观察者高出19%，整体准确率可达92%。本数据集包含主结果图（图2与图4）的原始数据，数据以CSV（Comma-Separated Values）与MAT两种文件格式提供，同时附带用于生成上述图表的MATLAB脚本文件。