Artificial intelligence; Chest radiographs; Cost-effectiveness; Health economics; Osteoporosis; Prevention; Screening; Humans; Female; Middle Aged; Aged; Germany; Quality-Adjusted Life Years; Markov Chains; Cost-Benefit Analysis; Deep Learning; Mass Screening/economics; Mass Screening/methods; Osteoporosis/diagnostic imaging; Osteoporosis/economics; Osteoporosis/diagnosis; Radiography, Thoracic/economics; Radiography, Thoracic/methods; Mass Screening; Radiography, Thoracic; Aging; Geriatrics and Gerontology
Abstract :
[en] [en] BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk, and improve public health outcomes.
AIMS: To assess the cost-effectiveness of deep learning models (hereafter referred to as AI-driven) applied to chest radiographs for opportunistic osteoporosis screening in German women aged 50 and older.
METHODS: A decision tree and microsimulation Markov model were used to calculate the cost per quality-adjusted life year (QALY) gained (€2024) for screening with AI-driven chest radiographs followed by treatment, compared to no screening and treatment. Patient pathways were based on AI model accuracy and German osteoporosis guidelines. Women with a fracture risk below 5% received no treatment, those with 5-10% risk received alendronate, and women 65 + with a risk above 10% received sequential treatment starting with romosozumab. Data was validated by a German clinical expert, incorporating real-world treatment persistence, DXA follow-up rates, and treatment initiation. Sensitivity analyses assessed parameter uncertainty.
RESULTS: The cost per QALY gained from screening was €13,340, far below the typical cost-effectiveness threshold of €60,000. Optimizing follow-up, treatment initiation, and medication adherence further improved cost-effectiveness, with dominance achievable by halving medication non-persistence, and in women aged 50-64.
CONCLUSION: AI-driven chest radiographs for opportunistic osteoporosis screening is a cost-effective strategy for German women aged 50+, with the potential to significantly improve public health outcomes, reduce fracture burdens and address healthcare disparities. Policymakers and clinicians should consider implementing this scalable and cost-effective screening strategy.
Disciplines :
Public health, health care sciences & services
Author, co-author :
Reginster, Jean-Yves ; Université de Liège - ULiège > Département des sciences de la santé publique ; Protein Research Chair, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
Schmidmaier, Ralf; Department of Medicine IV, LMU University Hospital, LMU Munich, Munich, Germany
Alokail, Majed; Protein Research Chair, Biochemistry Department, College of Science, King Saud University, Riyadh, Kingdom of Saudi Arabia
Hiligsmann, Mickael ; Department of Health Services Research, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands. m.hiligsmann@maastrichtuniversity.nl
Language :
English
Title :
Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.
Publication date :
13 May 2025
Journal title :
Aging Clinical and Experimental Research
ISSN :
1594-0667
eISSN :
1720-8319
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
JYR has received consulting fees from Promedius. RS is president of the German osteology association (DVO Dachverband Osteologie) and member of the DVO guideline committee for osteoporosis. He received consultancy funding from Alexion, Amgen, Sandoz and UCB, speeking fees from Alexion, Amgen, Blueprint, Takeda/Shire, Theramex and UCB. MA has no conflict of interest relevant to this paper. MH has received research grants (paid to institution) from Radius Health, and Angelini Pharma, lecture fees from IBSA (paid to institution) and Echolight, and was grant advisor for Pfizer (paid to institution) and consultant (paid to institution) for Gr\u00FCnenthal.This work was partly supported by the Distinguished Scientist Fellowship Program (DSFP) of the King Saud University, Riyadh, Kingdom of Saudi Arabia.
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