The Operating Theatre Journal May 2022

Machine learning outperforms clinical experts
in classifying hip fractures
Neural networks could improve patient outcomes and reduce
care costs
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 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
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 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 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 US and about £2 billion in the
UK.
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The neural networks were trained to recognise hip joints and
classify fractures
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 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 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
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 over 30 days.”
Mr Otto Von Arx, Consultant Orthopaedic Spinal Surgeon at Royal United
Hospitals Bath NHS Trust, and one of the paper co-authors, adds: “‘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.”
The study was funded by Arthroplasty for Arthritis Charity. The NVIDIA
Corporation provided the Titan X GPU that carried out the machine
learning, through their academic grant scheme.
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