Automatic Detection of Perilunate and Lunate Dislocations on Wrist Radiographs Using Deep Learning.

SUMMARY: Delayed or missed diagnosis of perilunate or lunate dislocations can lead to significant morbidity. Advances in computer vision provide an opportunity to improve diagnostic performance. In this study, a deep learning algorithm was utilized for detection of perilunate and lunate dislocations on lateral wrist radiographs. A total of 435 lateral wrist radiographs were labeled as normal or pathologic (perilunate or lunate dislocation). The lunate in each radiograph was segmented with a rectangular bounding box. Images were partitioned into training and test sets. Two neural networks, consisting of an object detector followed by an image classifier, were applied in series. First, the object detection module was used to localize the lunate. Next, the image classifier performed a binary classification for normal or pathologic. The accuracy, sensitivity, and specificity of the overall system were evaluated. A receiver operating characteristic (ROC) curve and the associated area under the curve (AUC) were used to demonstrate the overall performance of the computer vision algorithm. The lunate object detector was 97.0% accurate at identifying the lunate. Accuracy was 98.7% among the sub-group of normal wrist radiographs, and 91.3% among the sub-group of wrist radiographs with perilunate/lunate dislocations. The perilunate/lunate dislocation classifier had a sensitivity (recall) of 93.8%, specificity of 93.3%, and accuracy of 93.4%. The AUC was 0.986. We have developed a proof-of-concept computer vision system for diagnosis of perilunate/lunate dislocations on lateral wrist radiographs. This novel deep learning algorithm has potential to improve clinical sensitivity to ultimately prevent delayed or missed diagnosis of these injuries.

Electrostatic Charge on Flying Hummingbirds and Its Potential Role in Pollination.

Electrostatic phenomena are known to enhance both wind- and insect-mediated pollination, but have not yet been described for nectar-feeding vertebrates. Here we demonstrate that wild Anna's Hummingbirds (Calypte anna) can carry positive charges up to 800 pC while in flight (mean ± s.d.: 66 ± 129 pC). Triboelectric charging obtained by rubbing an isolated hummingbird wing against various plant structures generated charges up to 700 pC. A metal hummingbird model charged to 400 pC induced bending of floral stamens in four plants (Nicotiana, Hemerocallis, Penstemon, and Aloe spp.), and also attracted falling Lycopodium spores at distances of < 2 mm. Electrostatic forces may therefore influence pollen transfer onto nectar-feeding birds.