Artificial intelligence; chest radiographs; cost-effectiveness; osteoporosis; screening; Bone Density Conservation Agents; Humans; Female; United States; Middle Aged; Aged; Quality-Adjusted Life Years; Absorptiometry, Photon/economics; Models, Economic; Markov Chains; Bone Density Conservation Agents/therapeutic use; Cost-Benefit Analysis; Deep Learning/economics; Osteoporosis/diagnostic imaging; Osteoporosis/economics; Mass Screening/economics; Mass Screening/methods; Radiography, Thoracic/economics; Radiography, Thoracic/methods; Absorptiometry, Photon; Deep Learning; Mass Screening; Radiography, Thoracic; Radiology, Nuclear Medicine and Imaging
Abstract :
[en] [en] OBJECTIVES: Deep learning models applied to chest radiographs obtained for other clinical reasons have shown promise in opportunistic osteoporosis screening, particularly among middle-aged to older individuals. This study evaluates the cost-effectiveness of this approach in US women aged 50 years and over.
METHODS: An economic model, incorporating both a decision tree and a microsimulation Markov model, estimated the cost per quality-adjusted life-year (QALY) gained (in 2024 US dollars) for screening via chest radiographs with deep learning, followed by treatment, versus no screening and treatment. The patient pathways were based on the sensitivity and specificity of artificial intelligence-enhanced radiographs. Real-world medication persistence, realistic assumptions for probabilities of dual-energy x-ray absorptiometry examination postscreening detection and for treatment initiation rates were incorporated. Women with osteoporosis were stratified into high risk (receiving alendronate monotherapy for 5 years) and very high risk (receiving an 18-month anabolic treatment with abaloparatide followed by 5 years of alendronate). Parameter uncertainty was analyzed through sensitivity analyses.
RESULTS: The opportunistic screening strategy improved health outcomes, yielding more QALYs and fewer fractures while increasing treatment costs. The cost per QALY gained of opportunistic screening was estimated at $72,085 per QALY gained among women 50+, remaining below the US cost-effectiveness threshold of $100,000 per QALY. Further improvements in cost-effectiveness could be achieved by optimizing follow-up, treatment initiation, and medication adherence.
DISCUSSION: This study underscores the cost-effectiveness and public health value of opportunistic, artificial intelligence-driven screening osteoporosis screening using existing chest radiographs, demonstrating its potential to improve early detection and address unmet diagnostic needs in osteoporosis care.
Disciplines :
Public health, health care sciences & services
Author, co-author :
Hiligsmann, Mickaël ; Université de Liège - ULiège > Département des sciences de la santé publique > Santé publique, Epidémiologie et Economie de la santé ; Professor of Health Preferences and Economics of Prevention, Department of Health Services Research, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands. Electronic address: m.hiligsmann@maastrichtuniversity.nl
Silverman, Stuart L; Cedars-Sinai Medical Center, Los Angeles and the Osteoporosis Medical Center (OMC) Clinical Research Center, Beverly Hills, California
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, President of the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases
Language :
English
Title :
Cost-Effectiveness of Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning in the United States.
This work was supported by the Distinguished Scientist Fellowship Program of the King Saud University, Riyadh, Kingdom of Saudi Arabia. A research grant was received from Promedius partially covering the cost related to this study. The funder had no access to the dataset and model and had no role in study design, data collection or analysis.Jean-Yves Reginster, MD, PhD, reports educational research grant provided by Promedius; a relationship with Echolight, a relationship with Viatris, a relationship with Theramex HQ UK Limited, a relationship with Promedius, and a relationship with Besins that includes consulting or advisory and speaking and lecture fees. Mickael Hiligsmann, PhD, reports a relationship with Radius Health Inc and a relationship with Angelini Pharma that includes funding grants paid to institution; a relationship with IBSA Institut Biochimique SA and a relationship with Echolight that includes speaking and lecture fees; a relationship with Gr\u00FCnenthal GMBH and a relationship with Pfizer that includes consulting or advisory (paid to institution). Stuart Silverman, MD, reports a relationship with Amgen and a relationship with Radius Health Inc that includes consulting or advisory and funding grants, and a relationship with Promedius that includes consulting or advisory. The authors are non-partnership track/employees.
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