Lung allocation pipeline: Machine learning approach to optimized lung transplant

Emma Schinstock, Alex Deakyne, Tinen Iles, Andrew Shaffer, Paul A. Iaizzo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lung donation is the most risky transplant procedures. With low survival rates, and poor acceptance of donated lungs, those in need of a lung transplant are at high risk of dying. One reason for poor outcomes is the lack of optimal match between donor and recipient when it comes to lung size and shape. Lungs that do not properly fit in the recipient's chest cavity can fail to inflate fully and quickly start to deteriorate. In such patients, lung contusions can form, edema occurs in healthy lung tissue, and overall lung function declines. To improve patient outcomes after lung transplant, we describe here a developed a computational pipeline which enables donor lungs to be properly matched to recipients. This tool uses CT scans from both the donor and potential recipients to calculate how anatomically different the sets of lungs are, and therefore provide improved matches in both size and shape for the donor lungs.

Original languageEnglish (US)
Title of host publicationFrontiers in Biomedical Devices, BIOMED - 2020 Design of Medical Devices Conference, DMD 2020
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791883549
DOIs
StatePublished - 2020
Event2020 Design of Medical Devices Conference, DMD 2020 - Minneapolis, United States
Duration: Apr 6 2020Apr 9 2020

Publication series

NameFrontiers in Biomedical Devices, BIOMED - 2020 Design of Medical Devices Conference, DMD 2020

Conference

Conference2020 Design of Medical Devices Conference, DMD 2020
CountryUnited States
CityMinneapolis
Period4/6/204/9/20

Bibliographical note

Funding Information:
A special thank you to all those who gave the gift of donation through the Bequest program at the University of Minnesota. This research would not be possible without their donation.

Keywords

  • 3D segmentation
  • Lung transplant
  • Machine learning

Fingerprint Dive into the research topics of 'Lung allocation pipeline: Machine learning approach to optimized lung transplant'. Together they form a unique fingerprint.

Cite this