Abstract
Deep Convolutional Neural Networks as a Diagnostic Aid—A Step Toward Minimizing Undetected Scaphoid Fractures on Initial Hand Radiographs
Highlights
For radiographs run through their entire algorithm pipeline, 20 of 22 radiographically occult fractures were detected
These results indicate the potential of computer vision algorithms to eventually become a clinically meaningful tool for assessing possible scaphoid fractures in initial radiographs
The underlying rate of occult scaphoid fractures was 3.3% (161 of 4917 total fracture radiographs), with occult fracture radiographs representing 1.4% (161 of 11 838 total radiographs) of all radiographs included in the study
Summary
For radiographs run through their entire algorithm pipeline, 20 of 22 radiographically occult fractures were detected. These results indicate the potential of computer vision algorithms to eventually become a clinically meaningful tool for assessing possible scaphoid fractures in initial radiographs. When there is an imbalanced data set with few positives and the few positives are the class of interest (as is the case with occult scaphoid fractures), there are particular performance metrics that should be examined.
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