Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

ABSTRACT: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network. Maximal fracture scores resulted in a performant model for subject-level fracture prediction. Combining individual deep learning vertebral body fracture scores and demographic covariates for subject-level classification of osteoporotic fracture achieved excellent performance (AUC-ROC of 0.968) on a large dataset of radiographs with basic demographic data.

Purpose

Osteoporotic vertebral fractures are common and morbid. Automated opportunistic screening for incidental vertebral fractures from radiographs, the highest volume imagin

SUBMITTER: Cross NM 

PROVIDER: S-EPMC12282576 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature