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A Multimodal Disease Progression Model for Genetic Associations with Disease Dynamics

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14224))

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Abstract

We introduce a disease progression model suited for neurodegenerative pathologies that allows to model associations between covariates and dynamic features of the disease course. We establish a statistical framework and implement an algorithm for its estimation. We show that the model is reliable and can provide uncertainty estimates of the discovered associations thanks to its Bayesian formulation. The model’s interest is showcased by shining a new light on genetic associations.

This work was funded in part by the French government under management of Agence Nationale de la Recherche as part of the Investissements d’avenir program, reference ANR-19-P3IA- 0001 (PRAIRIE 3IA Institute), ANR-19-JPW2-000 (E-DADS), and ANR10-IAIHU-06 (IHU ICM), as well as by the European Research council reference ERC-678304 and the H2020 programme via grant 826421 (TVB-Cloud).

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Authors and Affiliations

  1. Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France

    Nemo Fournier & Stanley Durrleman

Authors
  1. Nemo Fournier
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  2. Stanley Durrleman
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Corresponding author

Correspondence to Nemo Fournier .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Fournier, N., Durrleman, S. (2023). A Multimodal Disease Progression Model for Genetic Associations with Disease Dynamics. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_58

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  • DOI: https://doi.org/10.1007/978-3-031-43904-9_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43903-2

  • Online ISBN: 978-3-031-43904-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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