AI-Driven opportunistic osteoporosis screening: the skeletal failure paradigm for trustworthy vertebral fracture triage
- 1Diagnostic and Interventional Radiology Department, Faculty of Medicine, Alexandria University
Abstract
Opportunistic CT screening for osteoporosis is moving toward clinical implementation, but progress is constrained by the absence of a standardized, machine learning compatible ground truth for vertebral fractures. Traditional semi-quantitative grading systems show only moderate inter-observer agreement, with ambiguity concentrated at mild deformities, introducing label noise that degrades AI triage performance. This invited review synthesizes the current evidence and proposes a hypothesis-generating framework; the Skeletal Failure paradigm is a proposal requiring prospective validation, not an established standard. Against this background, we outline the current landscape of AI-driven opportunistic CT tools and identify label inconsistency as a central barrier to reliable deployment. Drawing on this synthesis and the author’s clinical and research experience, we propose a binary, action-oriented classification—“Skeletal Failure”—defined by unequivocal endplate disruption or >20% vertebral height loss, providing a potentially reproducible structural endpoint. When combined with phantomless CT trabecular Hounsfield unit measurements, this framework is proposed as a candidate ML-ready substrate for automated triage and structured reporting. The Skeletal Failure paradigm is designed to support efficient human-in-the-loop triage, reduce radiologist cognitive load, and facilitate population-level fracture prevention. It may be particularly useful in resource-constrained settings, including the Middle East and North Africa, where DXA availability is limited and WHO T-score thresholds underperform in non-Caucasian populations. By converting incidental CT findings into consistent, machine-readable risk profiles, this approach aims to better align algorithmic detection with clinically actionable pathways, especially in older adults at risk of vertebral fragility and underdiagnosis.
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BibTeX
@article{nouh2026aidriven,
title = {{AI-Driven opportunistic osteoporosis screening: the skeletal failure paradigm for trustworthy vertebral fracture triage}},
author = {Mohamed Ragab Nouh},
journal = {The Egyptian Journal of Radiology and Nuclear Medicine},
year = {2026},
month = oct,
doi = {10.1186/s43055-026-01895-w},
url = {https://doi.org/10.1186/s43055-026-01895-w},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Driven opportunistic osteoporosis screening: the skeletal failure paradigm for trustworthy vertebral fracture triage
AU - Mohamed Ragab Nouh
PY - 2026
DA - 2026/10/25
JO - The Egyptian Journal of Radiology and Nuclear Medicine
DO - 10.1186/s43055-026-01895-w
UR - https://doi.org/10.1186/s43055-026-01895-w
AB - Opportunistic CT screening for osteoporosis is moving toward clinical implementation, but progress is constrained by the absence of a standardized, machine learning compatible ground truth for vertebral fractures. Traditional semi-quantitative grading systems show only moderate inter-observer agreement, with ambiguity concentrated at mild deformities, introducing label noise that degrades AI triage performance. This invited review synthesizes the current evidence and proposes a hypothesis-generating framework; the Skeletal Failure paradigm is a proposal requiring prospective validation, not an established standard. Against this background, we outline the current landscape of AI-driven opportunistic CT tools and identify label inconsistency as a central barrier to reliable deployment. Drawing on this synthesis and the author’s clinical and research experience, we propose a binary, action-oriented classification—“Skeletal Failure”—defined by unequivocal endplate disruption or >20% vertebral height loss, providing a potentially reproducible structural endpoint. When combined with phantomless CT trabecular Hounsfield unit measurements, this framework is proposed as a candidate ML-ready substrate for automated triage and structured reporting. The Skeletal Failure paradigm is designed to support efficient human-in-the-loop triage, reduce radiologist cognitive load, and facilitate population-level fracture prevention. It may be particularly useful in resource-constrained settings, including the Middle East and North Africa, where DXA availability is limited and WHO T-score thresholds underperform in non-Caucasian populations. By converting incidental CT findings into consistent, machine-readable risk profiles, this approach aims to better align algorithmic detection with clinically actionable pathways, especially in older adults at risk of vertebral fragility and underdiagnosis.
SN - 2090-4762
SN - 0378-603X
ER -