Deep Learning-based Opportunistic CT Osteoporosis Screening and the Establishment of Normative Values.

Publication/Presentation Date

11-1-2025

Abstract

Background Osteoporosis is underdiagnosed and undertreated, prompting the exploration of opportunistic screening using CT and artificial intelligence. Purpose To develop a reproducible convolutional neural network to automatically identify a three-dimensional (3D) region of interest (ROI) in trabecular bone, develop a correction method to normalize attenuation values across different CT protocols and scanner models, and establish thresholds for diagnosing osteoporosis in a large diverse population. Materials and Methods In this retrospective study, a deep learning-based method was developed to automatically quantify trabecular attenuation of the thoracic and lumbar spine on CT images with use of a 3D ROI. A statistical method was developed to adjust for different tube voltages and scanner models. Normative values and diagnostic thresholds for trabecular attenuation of the spine for osteoporosis were established based on the reported prevalence of osteoporosis by the World Health Organization. Differences between groups were assessed using the Student

Volume

317

Issue

2

First Page

250917

Last Page

250917

ISSN

1527-1315

Disciplines

Medicine and Health Sciences

PubMedID

41217284

Department(s)

Fellows and Residents

Document Type

Article

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