Classification of left atrial appendage morphology using Deep learning

Mikayle A. Holm, Alex Deakyne, Erik Gaasedelen, Weston Upchurch, Paul A. Iaizzo

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

Abstract

Atrial fibrillation, a common cardiac arrhythmia, can lead to blood clots in the left atrial appendage (LAA) of the heart, increasing the risk of stroke. Understanding the LAA morphology can indicate the likelihood of a blood clot. Therefore, a classification convolutional neural network was implemented to predict the LAA morphology. Using 2D images of 3D models created from MRI scans of fixed human hearts and a pre-trained network, an 8.7% error rate was achieved. The network can be improved with more data or expanded to classify the LAA from the automatically segmented DICOM datasets and measure the LAA ostia.

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

Keywords

  • Deep learning
  • Left atrial appendage

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