How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

In the emergency department, most suspected fractures are first assessed with plain radiography. This test is fast, low-cost, and easily available as a first-line imaging test when patients first present to an emergency department. However, errors often occur in the interpretation of these radiographs, particularly when fractures are missed because the clinical presentation and the interpretation of the radiograph do not match. This diagnostic discrepancy is one reason emergency X-ray interpretation remains vulnerable in high-volume settings.

‍

More than 340 radiology AI tools have received U.S. regulatory authorization, and adoption is increasing across radiology departments.¹ Fracture detection sits at the front of this adoption curve in emergency radiology because missed fractures represent one of the most common sources of diagnostic error in urgent imaging, and they are frequently cited in medicolegal claims against radiologists who interpret these studies.² Peer-reviewed meta-analytic evidence has demonstrated that fracture detection algorithms can reach diagnostic performance non-inferior to that of clinicians, and clinical evidence also shows that AI assistance can improve reader sensitivity when used to support fracture detection.³

‍

In most cases, the starting point for a project to detect bone fractures will be based on a real clinical or operational problem that exists today. For example, one of the goals of hospitals wanting to implement a bone fracture detection AI tool is to reduce their rate of missed fractures on X-rays, while another goal is to improve the consistency of interpretation between readers.

Furthermore, hospitals often want to support their clinical staff who face pressure to work quickly while completing diagnostic imaging; or they may wish to implement fracture detection technology due to the growing demand for imaging as the number of staff performing these exams continues to decrease.