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Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence

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Applications of Medical Artificial Intelligence (AMAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13540))

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Abstract

Geographic atrophy (GA) is an advanced form of age-related macular degeneration leading to progressive visual loss. The ability to accurately predict GA progression over time based on a single baseline visit can improve clinical trials in GA, as well as support patient counseling in current clinical practice. The feasibility of using baseline fundus autofluorescence (FAF) images to predict GA progression with end-to-end deep learning models has been demonstrated. However, for black-box models, there is a need to increase trust for clinical practice applications and estimate the prediction uncertainty. In this paper, we applied and evaluated both non-parametric and parametric deep ensemble approaches for the prediction uncertainty estimation using both simulated and clinical study data in a multitask regression setting. The results not only show promising performance in detecting near and far out-of-distribution data cases, but may also suggest the improved performance in predicting GA growth rate for in-distribution data.

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Authors and Affiliations

  1. Data Science Imaging, Genentech, Inc, South San Francisco, CA, USA

    Qi Yang, Jian Dai & Daniela Ferrara

  2. Roche Personalized Healthcare, Genentech, Inc, South San Francisco, CA, USA

    Qi Yang, Neha Anegondi, Verena Steffen, Simon S. Gao, Julia Cluceru, Christina Rabe, Jian Dai & Daniela Ferrara

  3. Clinical Imaging Group, Genentech, Inc, South San Francisco, CA, USA

    Neha Anegondi, Simon S. Gao & Julia Cluceru

  4. Biostatistics, Genentech, Inc, South San Francisco, CA, USA

    Verena Steffen & Christina Rabe

Authors
  1. Qi Yang
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  2. Neha Anegondi
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  3. Verena Steffen
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  4. Simon S. Gao
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  5. Julia Cluceru
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  6. Christina Rabe
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  7. Jian Dai
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  8. Daniela Ferrara
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Corresponding author

Correspondence to Qi Yang .

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Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shandong Wu

  2. National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA

    Behrouz Shabestari

  3. Stanford University, Stanford, CA, USA

    Lei Xing

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Yang, Q. et al. (2022). Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2022. Lecture Notes in Computer Science, vol 13540. Springer, Cham. https://doi.org/10.1007/978-3-031-17721-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-17721-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17720-0

  • Online ISBN: 978-3-031-17721-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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