RSNA Margulis Award Honors AI Research in X-Ray Imaging
The 2022 Radiological Society of North America (RSNA) Alexander R. Margulis Award for Scientific Excellence will be presented to Ali Guermazi, MD, PhD, for the Radiology article, Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence. He will receive the award during the RSNA 108th Scientific Assembly and Annual Meeting (RSNA 2022) in Chicago, Nov. 27-Dec. 1.
Named for Alexander R. Margulis, MD, a distinguished investigator and inspiring visionary in the science of radiology, this annual award recognizes the best original scientific article published in RSNA’s flagship journal, Radiology.
“This year’s Margulis Award recognizes the increasing importance of artificial intelligence in our field. The authors studied fracture detection by 24 radiologists and clinicians with and without AI,” said Radiology editor David A. Bluemke, MD, PhD. “Ten percent better fracture detection was present using AI, while reducing time for radiologists. This study validates the steady rise in the use of AI tools that are becoming a routine part of many clinical practices, particularly in musculoskeletal radiology.”
In the Food and Drug Administration (FDA) registration study, the researchers retrospectively analyzed 480 X-ray examinations from various U.S. hospitals.
“AI can be a powerful tool to help radiologists and other physicians improve diagnostic performance and increase efficiency, while potentially improving patient experience at the time of hospital or clinic visit,” said Guermazi, professor of radiology and medicine at BU Chobanian & Avedisian School of Medicine, and director of the Quantitative Imaging Center and chief of radiology at VA Boston Healthcare System.
The researchers included X-rays of limbs, pelvis, spine and rib cage. The exam group included adults over 21 years of age with indications of trauma and fracture prevalence of 50 percent. There were 240 patients with a total of 350 fractures, and 240 patients with no fractures.
The studies were analyzed twice by 24 U.S. board-certified readers from six different specialties including radiology, orthopedic surgery, rheumatology, emergency medicine (including physicians and physician assistants) and family medicine.
According to Guermazi, readings were performed both with and without a commercially developed software utilizing an algorithm trained on accurately annotated X-ray images from multiple institutions, acquired on a large variety of systems. Readers had a one-month period between the two analyses.
“The results of the study showed an absolute gain in sensitivity in the detection of fractures of 10.4 percent with the help of the software, with the software showing a sensitivity of 75.2 percent against 64.8 percent without the assistance of the software,” Guermazi said. The results also revealed an absolute gain in specificity—from 90.6 to 95.6 percent—for fracture detection with software assistance.
While not surprised by the algorithm’s sensitivity, Guermazi did not expect the gain in specificity.
“Computer-aided detection systems can be easily sensitive but usually bring significant loss in specificity. Here, the algorithm also helped reduce false-positive rates,” he said. “The time saving was a good surprise, given that the algorithm brings additional information to look at on top of the native images. It was not obvious that the algorithm would speed up interpretation time.
“Ultimately, I believe my radiology colleagues will join in viewing AI as a friend rather than a foe,” Guermazi said. “As it becomes clearer that it can beat the human eye at certain specific and repetitive or tedious tasks, AI will be viewed as a great add-on to heavy clinical workflow.”