Simulating Federated Learning to Enable Multi‐Hospital Collaboration for Lumbopelvic Alignment Estimation

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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Publication Details

Journal
JOR Spine
Published
2025-10-16
DOI
https://doi.org/10.1002/jsp2.70120
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Simulating Federated Learning to Enable Multi‐Hospital Collaboration for Lumbopelvic Alignment Estimation

Ferrán Pellisé, Ahmet Alanay, Andrea Cina, Javier Pizones et al.
JOR Spine
Medical Imaging and Analysis
article

Simulating Federated Learning to Enable Multi‐Hospital Collaboration for Lumbopelvic Alignment Estimation

Ferrán Pellisé, Ahmet Alanay, Andrea Cina, Javier Pizones, S. Richner-Wunderlin, Catherine R. Jutzeler, Frank Kleinstück, Ibrahim Obeid, Çağlar Yılgör, Yann Philippe Charles, Fabio Galbusera, Miklovana Tuci
article en

Abstract

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.

JOR SpineVol. 8(4)
Hebron University (PS), SIB Swiss Institute of Bioinformatics (CH), Université de Bordeaux (FR), Hospital Universitario La Paz (ES), Acıbadem University (TR), ETH Zurich (CH), Vall d'Hebron Institut de Recerca (ES), Bordeaux Population Health (FR), Schulthess-Klinik (CH), Universitätsklinik Balgrist (CH), Vall d'Hebron Hospital Universitari (ES), CIBBIM-Nanomedicine (ES), Université de Strasbourg (FR)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich
Openalex Percentile: Top 72%
Medical Imaging and Analysis
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