CT based Deep learning model may Predict subsequent fracture risk in Hip fracture cases

Key findings from the study are:

• The training and validation set had 1012 patients with a mean age of 74.5 years, including 706 females and 113 subsequent fractures.
• The test set had 468 patients of mean age, 75 years, with 335 females and 22 subsequent fractures.
• In the test set, the ensemble model had a higher C index (0.73) than other image-based models for predicting subsequent fractures (C index range, 0.59–0.70 for five of six models).
• The ensemble model achieved AUCs of 0.74, 0.74, and 0.73 at the follow-ups (2-, 3-, and 5 years), respectively. This is higher than most other image-based models at two years (AUC range, 0.57–0.71 for five of six models) and three years (AUC range, 0.55–0.72 for four of six models).
• Moreover, the ensemble model achieved higher AUCs than the clinical model [included known risk factors (AUCs of 0.58, 0.64, and 0.70, respectively in follow-up)].

Concluding further, among those with recent hip fractures, the ensemble deep learning model using digitally reconstructed radiographs from hip CT demonstrated good performance for predicting subsequent fractures in the short term.

Reference:

Kim, Y et al. A CT-based Deep Learning Model for Predicting Subsequent Fracture Risk in Patients with Hip Fracture. Radiology, 310(1).