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RECLAIM——一项回顾性、多中心观察性研究,旨在推动开发用于预测多发性硬化症疾病进展的人工智能驱动模型。

RECLAIM-A retrospective, multicenter observational study aimed at enabling the development of artificial intelligence-driven prognostic models for disease progression in multiple sclerosis.

作者信息

Praet Jelle, Anderhalten Lina, Comi Giancarlo, Horakova Dana, Ziemssen Tjalf, Vermersch Patrick, Lukas Carsten, Van Leemput Koen, Steppe Marjan, Manero Noemí, Kadas Ella, Bernard Alexis, van Rampelbergh Jean, de Boer Erik, Zingler Vera, Smeets Dirk, Ribbens Annemie, Paul Friedemann

机构信息

icometrix NV, Leuven, Belgium.

Experimental and Clinical Research Center (ECRC), A Cooperation Between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité - Universitätsmedizin Berlin, Berlin, Germany.

出版信息

Front Neurol. 2025 May 16;16:1557947. doi: 10.3389/fneur.2025.1557947. eCollection 2025.

Abstract

Multiple sclerosis (MS) is characterized by a progressive worsening of disability over time. As many regulatory-cleared disease-modifying treatments aiming to slow down this progression are now available, a clear need has arisen for a personalized and data-driven approach to treatment optimization in order to more efficiently slow down disease progression and eventually, progressive disability worsening. This strongly depends on the availability of biomarkers that can detect and differentiate between the different forms of disease worsening, and on predictive models to estimate the disease trajectory for each patient under certain treatment conditions. To this end, we here describe a multicenter, retrospective, observational study, aimed at setting up a harmonized database to allow the development, training, optimization, and validation of such novel biomarkers and AI-based decision models. Additionally, the data will be used to develop the tools required to better monitor this progression and to generate further insights on disease worsening and progression, patient prognosis, treatment decisions and responses, and patient profiles of patients with MS.

摘要

多发性硬化症(MS)的特点是随着时间的推移残疾状况逐渐恶化。由于现在有许多经监管批准旨在减缓这种进展的疾病修正治疗方法,因此迫切需要一种个性化且基于数据的方法来优化治疗,以便更有效地减缓疾病进展,并最终减缓进行性残疾恶化。这在很大程度上取决于能够检测并区分不同形式疾病恶化的生物标志物的可用性,以及用于估计每位患者在特定治疗条件下疾病轨迹的预测模型。为此,我们在此描述一项多中心、回顾性、观察性研究,旨在建立一个统一的数据库,以允许开发、训练、优化和验证此类新型生物标志物及基于人工智能的决策模型。此外,这些数据将用于开发更好监测这种进展所需的工具,并对疾病恶化与进展、患者预后、治疗决策与反应以及MS患者的患者概况产生进一步的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd8e/12124479/4b5852474e69/fneur-16-1557947-g001.jpg

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