25th January 2019 AI dramatically cuts time to process chest X-rays A new algorithm could help clear a backlog of chest radiographs, cutting the time required for an expert analysis from 11 days to less than three.
New research has found that a novel Artificial Intelligence (AI) system can dramatically reduce the time needed to ensure that abnormal chest X-rays with critical findings will receive an expert radiologist opinion sooner, cutting the average delay from 11 days to less than three. Chest X-rays are routinely performed to diagnose and monitor a wide range of conditions affecting the lungs, heart, bones, and soft tissues. Researchers at the University of Warwick, working with Guy's and St Thomas' NHS Hospitals, extracted a dataset of 500,000 anonymised adult chest radiographs (X-rays) and developed an AI system for computer vision, able to recognise abnormalities in real-time and suggest how quickly these exams should be reported by a radiologist. This included a Natural Language Processing (NLP) algorithm to read a radiological report, understand the findings mentioned by the radiologist, and automatically infer the priority level of the exam. By applying this algorithm to historical records, 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. Normal chest radiographs were detected with a positive predicted value of 73% and a negative predicted value of 99% – and at a speed that meant that the abnormal radiographs with critical findings could be prioritised to receive an expert opinion much sooner than the usual practice. "AI-led reporting of imaging could be a valuable tool to improve department workflow and workforce efficiency," said Giovanni Montana, Chair in Data Science at the University of Warwick. "The increasing clinical demands on radiology departments worldwide has challenged current service delivery models, particularly in publicly-funded healthcare systems. "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. In the United Kingdom, it is estimated that at any time there are over 300,000 radiographs waiting over 30 days for reporting. 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. The application of these technologies also extends to many other imaging modalities, including MRI and CT."
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