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The Role of Pleura and Adipose in Lung Ultrasound AI

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Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning (DCL 2021, PPML 2021, LL-COVID19 2021, CLIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12969))

Abstract

In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin’s space artifacts such as A-lines and B-lines) improved the AI model’s diagnostic accuracy.

G. R. Gare and W. Chen—Equal contribution.

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Acknowledgements

This present work was sponsored in part by US Army Medical contract W81XWH-19-C0083. We are pursuing intellectual property protection. Galeotti serves on the advisory board of Activ Surgical, Inc. He and Rodriguez are involved in the startup Elio AI, Inc.

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Authors and Affiliations

  1. Robotics Institute and Department of ECE, Carnegie Mellon University, Pittsburgh, USA

    Gautam Rajendrakumar Gare, Wanwen Chen, Alex Ling Yu Hung, Edward Chen & John Michael Galeotti

  2. Department of Pulmonary and Critical Care Medicine, Louisiana State University Health Sciences Center, New Orleans, USA

    Hai V. Tran, Tom Fox, Peter Lowery, Kevin Zamora & Bennett P. DeBoisblanc

  3. Cosmeticsurg.net, LLC, Baltimore, USA

    Ricardo Luis Rodriguez

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  1. Gautam Rajendrakumar Gare
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  2. Wanwen Chen
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  3. Alex Ling Yu Hung
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  5. Hai V. Tran
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  6. Tom Fox
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  7. Peter Lowery
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  8. Kevin Zamora
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  9. Bennett P. DeBoisblanc
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  10. Ricardo Luis Rodriguez
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  11. John Michael Galeotti
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Corresponding author

Correspondence to Gautam Rajendrakumar Gare .

Editor information

Editors and Affiliations

  1. Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura

  2. King's College London, London, UK

    M. Jorge Cardoso

  3. IBM Research-Haifa and Haifa University, Haifa, Israel

    Michal Rosen-Zvi

  4. Technical University of Munich, Munich, Germany

    Georgios Kaissis

  5. Children’s National Health System, Washington, D.C., DC, USA

    Marius George Linguraru

  6. Children’s National Health System, Washington, DC, USA

    Raj Shekhar

  7. Fraunhofer IGD, Darmstadt, Germany

    Stefan Wesarg

  8. Fraunhofer Singapore, Singapore, Singapore

    Marius Erdt

  9. Aachen University of Applied Sciences, Jülich, Germany

    Klaus Drechsler

  10. Tongji University, Shanghai, China

    Yufei Chen

  11. Helmholtz AI, Neuherberg, Germany

    Shadi Albarqouni

  12. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

  13. Vanderbilt University, Nashville, TN, USA

    Bennett Landman

  14. NVIDIA GmbH, Munich, Germany

    Nicola Rieke

  15. NVIDIA Corporation, Bethesda, MD, USA

    Holger Roth

  16. University of British Columbia, Vancouver, Canada

    Xiaoxiao Li

  17. NVIDIA Corporation, Santa Clara, CA, USA

    Daguang Xu

  18. IBM Research Europe, Rueschlikon, Switzerland

    Maria Gabrani

  19. Computer Vision Laboratory, Zürich, Switzerland

    Ender Konukoglu

  20. Assuta Medical Centers Radiology, Aviv-Yafo, Israel

    Michal Guindy

  21. Technical University of Munich, Munich, Germany

    Daniel Rueckert

  22. Technical University of Munich, Munich, Germany

    Alexander Ziller

  23. Technical University of Munich, Munich, Germany

    Dmitrii Usynin

  24. Imperial College London, London, UK

    Jonathan Passerat-Palmbach

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Gare, G.R. et al. (2021). The Role of Pleura and Adipose in Lung Ultrasound AI. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-90874-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90873-7

  • Online ISBN: 978-3-030-90874-4

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