External validation of a convolutional neural network algorithm for opportunistically detecting vertebral fractures in routine CT scans.

ABSTRACT: The Convolutional Neural Network algorithm achieved a sensitivity of 94% and specificity of 93% in identifying scans with vertebral fractures (VFs). The external validation results suggest that the algorithm provides an opportunity to aid radiologists with the early identification of VFs in routine CT scans of abdomen and chest.

Purpose

To evaluate the performance of a previously trained Convolutional Neural Network (CNN) model to automatically detect vertebral fractures (VFs) in CT scans in an external validation cohort.

Methods

Two Chinese studies and clinical data were used to retrospectively select CT scans of the chest, abdomen and thoracolumbar spine in men and women aged ≥50 years. The CT scans were assessed using the semiquantitative (SQ) Genant classification for pre

SUBMITTER: Nicolaes J 

PROVIDER: S-EPMC10786735 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature