Performance of a deep convolutional neural network for MRI-based vertebral body measurements and insufficiency fracture detection.

ABSTRACT:

Objectives

The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI.

Methods

This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coe

SUBMITTER: Germann C 

PROVIDER: S-EPMC10121505 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature