Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.

ABSTRACT:

Rationale and objectives

To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classification.

Materials and methods

A total of 456 participants were retrospectively included and were divided into a development cohort comprising 355 patients, with a 7:3 ratio randomly assigned to the training and validation cohorts, and a test cohort comprising 101 patients. The automatic segmentation model for the proximal femur was trained using VB-Net. The Dice similarity coefficient (DSC) and volume difference (VD) were employed to evaluate the performance of the segmentation model. A three-classification predictive model for as

SUBMITTER: Du C 

PROVIDER: S-EPMC12004748 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature