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Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study

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Applications of Medical Artificial Intelligence (AMAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14313))

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Abstract

Coronary atherosclerosis is a leading cause of morbidity and mortality worldwide. It is often treated by placing stents in the coronary arteries. Inappropriately placed stents or malappositions can result in post-interventional complications. Intravascular Ultrasound (IVUS) imaging offers a potential solution by providing real-time endovascular guidance for stent placement. The signature of malapposition is very subtle and requires exploring second-order relationships between blood flow patterns, vessel walls, and stents. In this paper, we perform a comparative study of various deep learning methods and their feature extraction capabilities for building a malapposition detector. Our results in the study address the importance of incorporating domain knowledge in performance improvement while still indicating the limitations of current systems for achieving clinically ready performance.

This work was funded in part by MIT-IBM Watson AI Lab.

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Authors and Affiliations

  1. IBM Research, San Jose, CA, 95120, USA

    Satyananda Kashyap, Niharika D’Souza & Tanveer Syeda-Mahmood

  2. Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

    Neerav Karani, Neel Dey, Lay Jain, Ray Wang & Polina Golland

  3. Boston Scientific, Maple Grove, MN, 55311, USA

    Alexander Shang, Hatice Akakin, Qian Li, Wenguang Li & Corydon Carlson

Authors
  1. Satyananda Kashyap
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  2. Neerav Karani
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  3. Alexander Shang
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  4. Niharika D’Souza
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  5. Neel Dey
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  6. Lay Jain
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  7. Ray Wang
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  8. Hatice Akakin
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  9. Qian Li
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  10. Wenguang Li
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  11. Corydon Carlson
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  13. Tanveer Syeda-Mahmood
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Corresponding author

Correspondence to Satyananda Kashyap .

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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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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Kashyap, S. et al. (2024). Feature Selection for Malapposition Detection in Intravascular Ultrasound - A Comparative Study. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_17

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  • DOI: https://doi.org/10.1007/978-3-031-47076-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47075-2

  • Online ISBN: 978-3-031-47076-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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