AI Helps Radiologists Speed and Improve Bone Fracture Diagnosis on X-Rays

Lack of timely access to expert opinion as the growth in imaging volumes continues to outpace radiologist recruitment only makes the problem worse. Artificial intelligence (AI) is an effective tool for fracture detection that has potential to aid clinicians in busy emergency departments, according to a new study.

The study by researchers at The Botnar Research Centre (Oxford, UK) showed that AI may help address the problem of missed or delayed diagnosis of fractures on X-ray by acting as an aid to radiologists, helping to speed and improve fracture diagnosis. To learn more about the technology’s potential in the fracture setting, the team of researchers reviewed 42 existing studies comparing the diagnostic performance in fracture detection between AI and clinicians. Of the 42 studies, 37 used X-ray to identify fractures, and five used CT. The researchers found no statistically significant differences between clinician and AI performance. AI’s sensitivity for detecting fractures was 91-92%.

The study results point to several promising educational and clinical applications for AI in fracture detection, according to the researchers. It could reduce the rate of early misdiagnosis in challenging circumstances in the emergency setting, including cases where patients may sustain multiple fractures. It also has potential as an educational tool for junior clinicians. However, the researchers have cautioned that their study of fracture detection by AI remains in a very early, pre-clinical stage. Only a minority of the studies that the team looked at evaluated the performance of clinicians with AI assistance, and there was only one example where an AI was evaluated in a prospective study in a clinical environment.

“We found that AI performed with a high degree of accuracy, comparable to clinician performance,” said study lead author Rachel Kuo, MBBCh, from the Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences in Oxford, England. “Importantly, we found this to be the case when AI was validated using independent external datasets, suggesting that the results may be generalizable to the wider population.”

“It could also be helpful as a ‘second reader,’ providing clinicians with either reassurance that they have made the correct diagnosis or prompting them to take another look at the imaging before treating patients,” added Dr. Kuo.

Related Links:
The Botnar Research Centre