AI model accurately detects universal trauma on pelvic radiographs: Study

"In this study, we developed an algorithm based on a point-supervised DL method that achieved high accuracy in identifying all kinds of trauma-related radiographic findings on PXRs. PelviXNet achieved an overall accuracy of 92.4% in a real-world population dataset," concluded the authors. 

"Prospective studies are now required to determine if PelvixNet could lead to more precise diagnoses in a clinical environment and facilitate trauma patient management."

About Trauma Management

Trauma management is a race against time. A timely, accurate diagnosis with appropriate management is the key to saving lives. High-quality clinical trauma care and treatment not only rely on physician experience but also require information from various imaging modalities. Pelvic radiography is one of the diagnostic imaging modalities commonly used to evaluate blunt trauma patients. Pelvic radiographs (PXRs) cover the pelvis and the upper femur area, providing diagnostic value for orthopedic injuries in these regions, including hip fracture, pelvic fracture, hip dislocation, and other associated injuries.

The study titled, "A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs," is published in the journal Nature Communications.

DOI: https://www.nature.com/articles/s41467-021-21311-3#Sec7