Now an AI tool to accurately locate hip fractures
Researchers at the University of Bath have developed two convolutional neural networks to identify and classify hip fractures from X-rays with a 19% higher accuracy compared to clinicians. The research team, fromc, as well as colleagues from the Royal United Hospitals Trust Bath, North Bristol NHS Trust, and Bristol Medical School, aims to help clinicians make hip fracture care more efficient to drive better patient outcomes. 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 Researchers used 3,659 hip X-rays to train and test the neural networks, and achieved an overall accuracy of 92%. “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,” said  Prof Richie Gill, lead author of the paper and co-director of the Center for Therapeutic Innovation.
Researchers used 3,659 hip X-rays to train and test the neural networks, and achieved an overall accuracy of 92%.
Researchers used 3,659 hip X-rays to train and test the neural networks, and achieved an overall accuracy of 92%.
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Sharath is an ardent believer in the ‘Transhumanism’ movement. Anything and everything about technology excites him. At Analytics India Magazine, he writes about artificial intelligence, cybersecurity and the impact these emerging technologies have on day-to-day human lives. When not working on a story, he spends his time reading tech novels and watching sci-fi movies and series.