Simulating Federated Learning to Enable Multi‐Hospital Collaboration for Lumbopelvic Alignment Estimation Article Swipe
Andrea Cina
,
Miklovana Tuci
,
Ferrán Pellisé
,
Çağlar Yılgör
,
Ahmet Alanay
,
Javier Pizones
,
Frank Kleinstück
,
Ibrahim Obeid
,
Yann Philippe Charles
,
S. Richner-Wunderlin
,
Fabio Galbusera
,
Catherine R. Jutzeler
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/jsp2.70120
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/jsp2.70120
Background Accurate computation of radiological parameters related to spinal alignment is clinically crucial for diagnosing and managing conditions, such as adolescent idiopathic scoliosis and adult spinal deformities. Key parameters, including sacral slope, pelvic tilt, pelvic incidence, and lumbar lordosis, are required to assess lumbosacral alignment. Artificial Intelligence (AI) has demonstrated strong potential in automating these assessments, reducing clinician workload and improving consistency. However, AI models require large, diverse, high‐quality datasets to perform reliably across different clinical settings. Privacy concerns and data ownership issues often hinder data sharing, limiting the creation of centralized datasets. Methods In this study, we demonstrate that federated learning (FL) enables the training of deep learning models across four hospitals without compromising patient privacy. In particular, we compared FL against a centralized approach, where data from all the hospitals are pooled together and a model is trained on them, and a local approach consisting of training individual models exclusively on data from each respective hospital, resulting in distinct hospital‐specific models. Results FL achieved performance comparable to centralized training (errors ~5°), where data is pooled, and consistently outperformed models trained on data from individual hospitals, both in internal (~8°) and external (~10°) testing. Conclusion This work highlights FL as a viable solution for collaborative AI development in spinal imaging, facilitating the use of diverse, multi‐institutional data while circumventing privacy barriers and complex data‐sharing agreements. Additionally, FL demonstrates particular benefits for smaller hospitals, enabling them to achieve superior model performance by effectively leveraging data from hospitals with larger datasets.
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- https://doi.org/10.1002/jsp2.70120
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jsp2.70120
- OA Status
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https://openalex.org/W4415251818Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/jsp2.70120Digital Object Identifier
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Simulating Federated Learning to Enable Multi‐Hospital Collaboration for Lumbopelvic Alignment EstimationWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-10-16Full publication date if available
- Authors
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Andrea Cina, Miklovana Tuci, Ferrán Pellisé, Çağlar Yılgör, Ahmet Alanay, Javier Pizones, Frank Kleinstück, Ibrahim Obeid, Yann Philippe Charles, S. Richner-Wunderlin, Fabio Galbusera, Catherine R. JutzelerList of authors in order
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jsp2.70120Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/jsp2.70120Direct OA link when available
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