Machine learning outperforms clinical experts in classifying hip
fractures
Neural networks could improve patient outcomes and reduce care costs
Date:
February 11, 2022
Source:
University of Bath
Summary:
A new machine learning process designed to identify and classify
hip fractures has been shown to outperform human clinicians. Two
convolutional neural networks (CNNs) were able to identify and
classify hip fractures from X-rays with a 19% greater degree of
accuracy and confidence than hospital-based clinicians.
FULL STORY ==========================================================================
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 -L-2 billion in
the UK.
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.
========================================================================== Story Source: Materials provided by University_of_Bath. Note: Content
may be edited for style and length.
========================================================================== Journal Reference:
1. E. A. Murphy, B. Ehrhardt, C. L. Gregson, O. A. von Arx,
A. Hartley, M.
R. Whitehouse, M. S. Thomas, G. Stenhouse, T. J. S. Chesser,
C. J. Budd, H. S. Gill. Machine learning outperforms clinical
experts in classification of hip fractures. Scientific Reports,
2022; 12 (1) DOI: 10.1038/s41598-022-06018-9 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/02/220211102644.htm
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