A study published in Radiology: Artificial Intelligence found that convolutional neural networks could detect and show fractures on wrist radiographs with a high level of sensitivity and specificity.
A dataset of 7356 wrist radiographs was split into training (90%) and validation (10%) sets. The models were tested on an unseen test set of 524 consecutive emergency wrist radiographic studies with two radiologists in consensus as the reference standard.
The model detected and correctly localized 310 (91.2%) of 340 and 236 (96.3%) of 245 of all radius and ulna fractures on the frontal and lateral views, respectively. The per-study sensitivity, specificity were 98.1%, and 72.9%, respectively.
The authors concluded that convolutional neural networks were able to detect and localize radius and ulna fractures on wrist radiographs with high sensitivity and specificity.
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