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WEDNESDAY, Oct. 24, 2018 (HealthDay News) — Deep learning methods allow senior medical specialists to deliver their expertise to emergency medicine clinicians via use of a deep neural network, which is associated with considerable improvement in sensitivity and specificity of detecting fractures in wrist radiographs, according to a study published online Oct. 22 in the Proceedings of the National Academy of Sciences<\/i>.<\/p>\n
Robert Lindsey, Ph.D., from Imagen Technologies in New York City, and colleagues developed a deep neural network to detect and localize fractures in radiographs. The researchers had 18 senior subspecialized orthopedic surgeons annotate 135,409 radiographs to train the network to accurately emulate their expertise. A controlled experiment was then run to examine the ability of emergency medicine clinicians to detect fractures in wrist radiographs with and without the assistance of the deep learning model.<\/p>\n
The researchers found that for the average clinician, sensitivity was 80.8 and 91.5 percent when unaided and aided, respectively, and the corresponding specificity was 87.5 and 93.9 percent. There was a 47 percent relative reduction in misinterpretation rate for the average clinician.<\/p>\n
“This study shows that deep learning models offer potential for subspecialized clinicians (without machine learning experience) to teach computers how to emulate their diagnostic expertise and thereby help patients on a global scale,” the authors write.<\/p>\n
The authors are affiliated with Imagen Technologies, which funded the research.<\/p>\n
Image courtesy of Imagen Technologies.<\/p>\n