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AI can dramatically cut time needed to process abnormal chest X-rays

A new Artificial Intelligence (AI) system can dramatically reduce the time needed to ensure that chest X-rays with critical findings will receive an expert radiologist opinion sooner, according to new research.

Developed by scientists from WMG at the University of Warwick and Guy’s and St Thomas’ NHS Hospitals, the new system can cut the average delay from 11 days to less than three days.

“In the United Kingdom, it is estimated that at any time there are over 300,000 radiographs waiting over 30 days for reporting”, WMG’s Professor Giovanni Montana who led the study said, “the results of this research shows that alternative models of care, such as computer vision algorithms, could be used to greatly reduce delays in the process of identifying and acting on abnormal X-rays – particularly for chest radiographs which account for 40% of all diagnostic imaging performed worldwide.”

Chest X-rays are regularly performed to diagnose and monitor a wide range of conditions affecting the lungs, heart, bones, and soft tissues.

The researchers who worked on the study analysed half million anonymised adult chest radiographs (X-rays) and developed an AI system for computer vision that can recognise radiological abnormalities in real-time and suggest how quickly these exams should be reported by a radiologist.

After analysing the data, the team generated a large volume of training exams that allowed the AI system to understand which visual patterns in X-rays were predictive of their urgency level.

Montana noticed how artificial intelligence led reporting of imaging could be a valuable tool to improve department workflow and workforce efficiency.

“The increasing clinical demands on radiology departments worldwide has challenged current service delivery models, particularly in publicly-funded healthcare systems”, he said. “It is no longer feasible for many Radiology departments with their current staffing level to report all acquired plain radiographs in a timely manner, leading to large backlogs of unreported studies.”

The results of the research were published today in the leading journal Radiology in a paper entitled “Automated triaging and prioritisation of adult chest radiographs using deep artificial neural networks.”


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