Machine learning outperforms clinical experts in classification of hip fractures
Ellen Murphy, Beate Ehrhardt, Celia L. Gregson, Otto A. Von Arx, April Hartley, Michael R. Whitehouse, Marie Thomas, Gregor Stenhouse, T.J.S. Chesser, Chris Budd, Harmandeep Singh Gill
Abstract
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%.