Automated vertebral compression fracture detection and quantification on opportunistic CT scans: a performance evaluation.

D Guenoun¹, M S Quemeneur², A Ayobi³, C Castineira⁴, S Quenet⁴, J Kiewsky⁴, M Mahfoud⁴, C Avare⁴, Y Chaibi⁴, P Champsaur²

¹Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France; Institute of Movement Sciences (ISM), CNRS, Aix Marseille University, 13005 Marseille, France.

²Department of Radiology, Institute for Locomotion, Sainte-Marguerite Hospital, APHM, 13009 Marseille, France.

³Avicenna.AI, 375 Avenue Du Mistral, 13600 La Ciotat, France. Electronic address: angela.ayobi@avicenna.ai.

⁴Avicenna.AI, 375 Avenue Du Mistral, 13600 La Ciotat, France.

PubMed DOI

Abstract

AIM: Since the majority of vertebral compression fractures (VCFs) are asymptomatic, they often go undetected on opportunistic CT scans. To reduce rates of undiagnosed osteoporosis, we developed a deep learning (DL)-based algorithm using 2D/3D U-Nets convolutional neural networks to opportunistically screen for VCF on CT scans. This study aimed to evaluate the performance of the algorithm using ext…

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