Machine Learning Can Assist in Hip Fracture Classification

A new machine-learning method used to identify and classify hip fractures has demonstrated its ability to outperform human clinicians.

Two convolutional neural networks (CNNs) developed at the University of Bath identified and classified hip fractures from X-rays with a 19 percent greater degree of accuracy and confidence than hospital-based clinicians.

The researchers from Bath’s Centre for Therapeutic Innovation and Institute for Mathematical Innovation and their colleagues from the Royal United Hospitals Trust Bath, North Bristol NHS Trust, and Bristol Medical School, set out to create a new process to assist clinicians in making hip fracture care more efficient.

The team used a total of 3,659 hip X-rays, classified by at least two experts, to train and test the machine-learning neural networks, which achieved an overall accuracy of 92 percent.

Hip fractures are a significant cause of morbidity and mortality in the elderly, creating high health costs and challenges to social care. Classifying a fracture before surgery is critical to assist surgeons in selecting the right interventions to treat the fracture, restore mobility, and improve patient outcomes.

The ability to accurately and quickly classify a fracture is critical. According to the press release, delays to surgery greater than 48 hours can increase the risk of adverse outcomes and mortality.

Depending on the part of the joint it occurs in, fractures are divided into three classes: intracapsular, trochanteric, or subtrochanteric. Some treatments 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. Given the projections for population aging over the coming decades, the number of hip fractions is predicted to increase globally.  

Additionally, those who sustain a hip fracture have twice the age-specific mortality of the general population in the following year, making the development of strategies to improve hip fracture management a high priority. 

A critical issue impacting the use of diagnostic imaging is the mismatch between demand and resource. For instance, the number of radiographs performed annually has increased by 25 percent from 1996 to 2014 in the United Kingdom. Rising demands often mean that departments cannot report results promptly. This is where machine learning can help.

“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 UK – whether this is done by orthopedic surgeons or radiologists specializing in musculoskeletal disorders,” lead author of the paper and Co-Director of the Center for Therapeutic Innovation Richie Gill said in the press release. “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 UK for over 30 days.”

Otto Von Arx, consultant orthopedic spinal surgeon at Royal United Hospitals Bath NHS Trust, and one of the paper co-authors, added, “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. This study demonstrates the excellent value of collaboration by the RUH and the research leader, the University of Bath.”