Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection—DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set—average precision of 0.99 (0.99–0.99) versus 0.64 (0.46–0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98–0.99) versus 0.84 (0.72–0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.

In a continued focus on improving digital health technology, the United States Food and Drug Administration has permitted marketing of an artificial intelligence (AI)-based algorithm for detection of wrist fractures. The software, known as OsteoDetect, effectively identifies distal radius fractures in two-dimensional X-ray images. The device is intended as an adjunct and not a replacement for clinician review of radiographs, the FDA noted. In retrospective studies submitted to the FDA for the approval, use of the device increased sensitivity and specificity as well as both positive and negative predictive values when compared with standard methods. "Artificial intelligence algorithms have tremendous potential to help health care providers diagnose and treat medical conditions," said Robert Ochs, PhD, the acting deputy director for radiological health in the Office of In Vitro Diagnostics and Radiological Health at the FDA's Center for Devices and Radiological Health, in a statement.