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Diagnosis

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AI and Big Data in Cardiology

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Abstract

This chapter covers the clinical application of diagnosis of cardiovascular disease. A clinical opinion piece discusses the current clinical standard for diagnosis tasks and its limitations. The technical review summarizes the classical machine learning pipeline for medical diagnosis as well as some common types of traditional machine learning models that have been used for this application. Following this, some relevant deep learning architectures for computer-aided diagnosis are discussed. Some example applications of artificial intelligence based automated diagnosis are introduced and the key challenges highlighted. The practical tutorial deals with a simple diagnosis task based on characteristics derived from cardiac MR segmentations and other patient characteristics. The chapter closes with a clinical opinion piece that speculates on the future role of AI in cardiac diagnosis.

Authors’ contribution:

\(\bullet \) Introduction, Opinion: RR.

\(\bullet \) Main chapter: DR, MK, GK.

\(\bullet \) Tutorial: ND.

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Notes

  1. 1.

    Editors’ note: There is some overlap in content between these descriptions and those provided in Chap. 4 but we choose to include both as we believe they act as complementary perspectives on these important concepts.

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Acknowledgements

ND was supported by the French ANR (LABEX PRIMES of Univ. Lyon [ANR-11-LABX-0063] within the program “Investissements d’Avenir” [ANR-11-IDEX-0007], and the JCJC project “MIC-MAC” [ANR-19-CE45-0005]).

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Authors and Affiliations

  1. Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany

    Daniel Rueckert, Moritz Knolle & Georgios Kaissis

  2. Department of Computing, Imperial College London, London, United Kingdom

    Daniel Rueckert, Moritz Knolle & Georgios Kaissis

  3. Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany

    Moritz Knolle & Georgios Kaissis

  4. Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France

    Nicolas Duchateau

  5. Institut Universitaire de France (IUF), Paris, France

    Nicolas Duchateau

  6. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Reza Razavi

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Correspondence to Daniel Rueckert .

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  1. CREATIS, Lyon 1 University, Villeurbanne Cedex, France

    Nicolas Duchateau

  2. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Andrew P. King

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Rueckert, D., Knolle, M., Duchateau, N., Razavi, R., Kaissis, G. (2023). Diagnosis. In: Duchateau, N., King, A.P. (eds) AI and Big Data in Cardiology. Springer, Cham. https://doi.org/10.1007/978-3-031-05071-8_5

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