Deep learning-based quantitative morphological study of anteroposterior digital radiographs of the lumbar spine.

ABSTRACT:

Background

Morphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance.

Methods

This study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by thr

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11320550 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature