The team trained and tested the solution with 3,659 hip X-rays that were classified by two experts. It showed overall accuracy of 92% compared to the 77.5% shown by the clinicians. While accuracy varied depending on the fracture, the researchers said the algorithms showed “an impressive, and potentially significant” ability to classify them.

"We are looking at both expanding the capabilities of this system for classifying hip fractures into more refined subtypes and using this type of approach for other imaging modalities such as CT and PET. Three-dimensional imaging contains a huge amount of data, and conventionally, it is reported by looking at 2D views of the data set; this is an area in which AI methods may be able to exploit all the available information," said Gill.


He adds, however, that despite outperforming humans in this study, the AI technology is not meant to, and unlikely to, replace radiologists. "The machine learning systems at the moment can only identify pathologies they have been trained on. Perhaps more importantly, a human observer can instantly tell if the correct image has been presented. For example, if a knee radiograph is wrongly labelled as a hip radiograph in the imaging system and then passed on for reporting for hip pathology, the human will immediately realize it is the wrong image. This is not so straightforward for the AI system, and considerable effort would be needed for AI systems to be able to establish that the correct image is being used for a given reporting stream."

The study was funded by the Arthroplasty for Arthritis Charity. The NVIDIA Corporation provided the Titan X GPU that carried out the machine learning.

The findings were published in Nature Scientific Reports.

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