A novel machine learning process developed at the University of Bath to identify and classify hip fractures outperforms human clinicians by 19%, found a study in Nature Scientific Reports.
Two convolutional neural networks (CNNs) developed at the University of Bath were able to identify and classify hip fractures from X-rays with a 19% greater degree of accuracy and confidence than hospital-based clinicians.
The research team, from Bath’s Centre for Therapeutic Innovation and Institute for Mathematical Innovation, as well as colleagues from the Royal United Hospitals Trust Bath, North Bristol NHS Trust, and Bristol Medical School, created the new process to help clinicians make hip fracture care more efficient and to support better patient outcomes.
They used a total of 3,659 hip X-rays, classified by at least two experts, to train and test the neural networks, which achieved an overall accuracy of 92%, and 19% greater accuracy than hospital-based clinicians.
Effective treatment is crucial in managing high costs
Hip fractures are a major cause of morbidity and mortality in the elderly, incurring high costs to health and social care. Classifying a fracture prior to surgery is crucial to help surgeons select the right interventions to treat the fracture and restore mobility and improve patient outcomes.
The ability to swiftly, accurately, and reliably classify a fracture is key: delays to surgery of more than 48 hours can increase the risk of adverse outcomes and mortality.
Fractures are divided into three classes: intracapsular, trochanteric, or subtrochanteric, depending on the part of the joint in which they occur. Some treatments, which are determined by the fracture classification, can cost up to 4,5 times as much as others.
In 2019, a total of 67,671 hip fractures were reported to the UK National Hip Fracture Database, and given projections for population ageing over the coming decades, numbers are predicted to increase globally, particularly in Asia. Across the world, an estimated 1,6m hip fractures occur annually with substantial economic burden, around $6bn per year in the US and about £2bn in the UK.
Longer-term patient outcomes are equally important: people who fracture a hip have, in the following year, twice the age-specific mortality of the general population. So, the team says, the development of strategies to improve hip fracture management and their impact of morbidity, mortality and healthcare provision costs is a high priority.
Rising demand on radiology departments
One critical issue affecting the use of diagnostic imaging is the mismatch between demand and resource: for example, in the UK the number of radiographs (including X-rays) performed annually has increased by 25% from 1996 to 2014. Rising demand on radiology departments often means they cannot report results in a timely manner.
Prof Richie Gill, lead author of the paper and co-director of the Centre for Therapeutic Innovation, says: “Machine-learning methods and neural networks offer a new and powerful approach to automate diagnostics and outcome prediction, so this new technique we’ve shared has great potential. Despite fracture classification so strongly determining surgical treatment and hence patient outcomes, there is currently no standardised process as to who determines this classification in the UK, whether this is done by orthopaedic surgeons or radiologists specialising in musculoskeletal disorders.
“The process we’ve developed could help standardise that process, achieve greater accuracy, speed up diagnosis and alleviate the bottleneck of 300,000 radiographs that remain unreported in the UK for more than 30 days.”
Otto Von Arx, consultant orthopaedic spinal surgeon at Royal United Hospitals Bath NHS Trust, and one of the paper co-authors, said: “As trauma clinicians, we constantly strive to deliver excellence of care to our patients and the healthcare community underpinned by accurate diagnosis and cost-effective medicine.
“This excellent study has provided us with an additional tool to refine our diagnostic armamentarium to provide the best care for our patients, and demonstrates the excellent value of collaboration by the RUH and the research leader, the University of Bath.”
Study details
Machine learning outperforms clinical experts in classification of hip fractures.
E. Murphy, B. Ehrhardt, C. Gregson, O. von Arx, A. Hartley, M. Whitehouse, M. Thomas, G. Stenhouse, T. Chesser, C. Budd, H. Gill.
Published in Nature Scientific Reports on 8 February 2022.
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 3,659 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%.
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