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Artificial Intelligence Approaches to the Imaging of Neurodegenerative Diseases

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Molecular Imaging of Neurodegenerative Disorders

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Abstract

We are living in a time of rapid progress in artificial intelligence. Uses for medicine and more specifically neuroimaging are exploding across a broad horizon, ranging from image quality improvement to autonomous diagnosis of medical images. For neurodegenerative disease, there are a host of applications, from creating better upstream image data quality, cross-modality transformation to synthesize missing data, to classification of current and future biomarkers and disease states. In many cases, these applications will improve access to neuroimaging technology at reduced cost and risk. In this chapter, we will explore the existing literature, speculate on future use cases, and discuss the challenge of AI, including generalizability, bias, and explicability. Physicians, imaging physicists, biomedical engineers, and other domain experts must engage these new topics to best optimize AI that serves patients with neurodegenerative disease.

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Authors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Greg Zaharchuk

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  1. Greg Zaharchuk
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Correspondence to Greg Zaharchuk .

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Editors and Affiliations

  1. Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA

    Donna J. Cross

  2. Department of Nuclear Medicine, Hospital das Forças Armadas (HFA), Brasilia, Distrito Federal, Brazil

    Karina Mosci

  3. Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA

    Satoshi Minoshima

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Zaharchuk, G. (2023). Artificial Intelligence Approaches to the Imaging of Neurodegenerative Diseases. In: Cross, D.J., Mosci, K., Minoshima, S. (eds) Molecular Imaging of Neurodegenerative Disorders. Springer, Cham. https://doi.org/10.1007/978-3-031-35098-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-35098-6_14

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