Using deep learning with CT helps predict hip fractures

A deep-learning model that includes digital x-rays reconstructed from 3D hip CT images shows promise for predicting subsequent fractures in patients with a relatively recent hip break, researchers have found. The findings could improve how clinicians care for their patients, wrote a team led by doctoral candidate Yisak Kim of Seoul National University Graduate School in South Korea. The group's findings were published January 30 in Radiology. "Developing a prediction model for short-term subsequent fracture risk is important because it would identify patients at the highest risk and aid in determining appropriate treatment strategies," the team noted. Patients who sustain an initial fracture are at highest risk for another over the next few years, the investigators explained. But models to predict short-term subsequent risk have not been developed. To fill this knowledge gap, Kim and colleagues developed and validated a deep-learning prediction model for subsequent fracture risk using digitally reconstructed x-rays from hip CT images in patients with recent hip fractures. Their research included 1,480 adults who underwent 3D hip CT imaging prompted by a fracture between January 2004 and December 2020. Mean follow-up time was 3.4 years, and primary outcomes were subsequent bone fractures in the spine, hip, humerus, or wrist, occurring at least three months after the initial break. The team generated 2D frontal, lateral, and axial digitally reconstructed x-rays from the CT ...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Clinical News Subspecialties CT Digital X-Ray Musculoskeletal Radiology Source Type: news