Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images.

ABSTRACT:

Objective

Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness.

Methods

In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was ev

SUBMITTER: Tong X 

PROVIDER: S-EPMC10813496 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature