Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model.

ABSTRACT: A multi-feature fusion DCNN model for automated evaluation of lumbar vertebrae L1 on chest combined with clinical information and radiomics permits estimation of volumetric bone mineral density for evaluation of osteoporosis.

Purpose

To develop a multi-feature deep learning model based on chest CT, combined with clinical information and radiomics to explore the feasibility in screening for osteoporosis based on estimation of volumetric bone mineral density.

Methods

The chest CT images of 1048 health check subjects were retrospectively collected as the master dataset, and the images of 637 subjects obtained from a different CT scanner were used for the external validation cohort. The subjects were divided into three categories according to the quantitative CT (QCT) examination

SUBMITTER: Pan J 

PROVIDER: S-EPMC11485148 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature