Key Points
Question
Can deep convolutional neural networks (DCNNs) detect occult scaphoid fractures not visible to human observers?
Findings
In this diagnostic study of 11 838 scaphoid radiographs, the DCNN trained to distinguish scaphoid fractures from scaphoids without fracture achieved an overall sensitivity and specificity of 87.1% and 92.1%, respectively, with an area under the receiver operating curve (AUROC) of 0.955; a second DCNN, which examined negative cases from the first DCNN, achieved a sensitivity and specificity of 79.0% and 71.6% with an AUROC of 0.810. This 2-stage DCNN model correctly identified 90% of occult fractures.
Meaning
These findings suggest that DCNNs can be trained to reliably detect fractures of small bones, such as scaphoids, and may be able to assist with radiographic detection of occult fractures that are not visible to human observers.
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