A new machine learning process designed to identify and classify hip fractures has been shown to outperform human clinicians. Two convolutional neural networks (CNNs) developed at the U.K.-based 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, in results published this week in Nature Scientific Reports.

The research team, from Bath’s Centre for Therapeutic Innovation and Institute for Mathematical Innovation, as well as colleagues from the U.K.-based Royal United Hospitals Trust Bath, North Bristol NHS Trust, and Bristol Medical School, set about creating 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 classify a fracture swiftly, accurately, and reliably 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 they occur in. Some treatments, which are determined by the fracture classification, can cost up to 4.5 times as much as others.

In 2019, 67,671 hip fractures were reported to the UK National Hip Fracture Database and given projections for population ageing over the coming decades, the number of hip fractures is predicted to increase globally, particularly in Asia. Across the world, an estimated 1.6 million hip fractures occur annually with substantial economic burden—approximately $6 billion per year in the U.S. and about £2 billion in the U.K.

As important are longer-term patient outcomes: People who sustain a hip fracture 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 U.K. 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.

Richie Gill, lead author of the paper and co-director of the Center 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 standardized process as to who determines this classification in the U.K.—whether this is done by orthopedic surgeons or radiologists specializing in musculoskeletal disorders.”

“The process we’ve developed could help standardize that process, achieve greater accuracy, speed up diagnosis and alleviate the bottleneck of 300,000 radiographs that remain unreported in the U.K. for over 30 days,” Gill adds.