No significant difference in AUC seen for detecting scaphoid fractures with convolutional neural network versus 11 radiologists<\/p>\n<\/h3>\n
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FRIDAY, May 21, 2021 (HealthDay News) — A convolutional neural network (CNN) has been developed for detecting scaphoid fractures on radiographs of the hand, wrist, and scaphoid, according to a report published online April 28 in Radiology: Artificial Intelligence<\/em>.<\/p>\n Nils Hendrix, from Jeroen Bosch Ziekenhuis in the Netherlands, and colleagues compared the performance of a CNN to 11 radiologists for detecting scaphoid fractures on conventional radiographs of the hand, wrist, and scaphoid. Three datasets from two hospitals were reviewed: a dataset of 1,039 radiographs (775 patients) for developing a scaphoid segmentation CNN; a dataset of 3,000 radiographs (1,846 patients) for developing a scaphoid fracture detection CNN; and a dataset of 190 radiographs (190 patients) for testing the complete fracture detection system.<\/p>\n The researchers found that a Dice similarity coefficient of 97.4 \u00c2\u00b1 1.4 percent with a Hausdorff distance 1.31 mm \u00c2\u00b1 1.03 was achieved with the segmentation CNN. Sensitivity and specificity of the detection CNN were 78 and 84 percent, respectively; positive predictive value was 83 percent; and the area under the receiver operating characteristic curve (AUC) was 0.87. No significant difference was seen between the AUC of the CNN and radiologists (0.87 versus 0.83).<\/p>\n “The system may be able to assist residents, radiologists, or other physicians by acting either as a first or second reader, or as a triage tool that helps prioritize worklists, potentially reducing the risk of missing a fracture,” Hendrix said in a statement.<\/p>\n Two authors disclosed financial ties to the medical device industry.<\/p>\n Abstract\/Full Text (subscription or payment may be required)<\/a><\/p>\n <\/i><\/p>\n