Application of machine learning for detection of hypertrophic cardiomyopathy patients from echocardiogram measurements

Nasibeh Zanjirani Farahani, Moein Enayati, Divaakar Siva Baala Sundaram, Devanshi Damani, Vinod C. Kaggal, April L. Zacher, Jeffrey B. Geske, Garvan Kane, Shivaram Poigai Arunachalam, Kalyan S Pasupathy, Adelaide M Arruda-Olson

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

Abstract

Hypertrophic cardiomyopathy (HCM) is a heritable, phenotypically diverse, and often asymptomatic heart muscle disease which is a major cause of sudden cardiac death (SCD) in young adults. The gold-standard for the diagnosis of HCM is echocardiography (echo), which is an ultrasound-based cardiac imaging modality. Across all sites of the Mayo Clinic enterprise, echo images and measurement data are reviewed, interpreted, and reported via the Echo Information Management System (EIMS). The objective of this paper is to develop a machine learning model for the identification of HCM from cardiac measurements obtained by the echo. We developed a novel machine learning model on patient demographic information and echo measurements that were retrieved from the EIMS digital data registry and selected by cardiologists. Random forest (RF) was utilized to investigate the predictive performance of these features on the identification of HCM patients. The HCM cohort consists of 3,548 patients with at least one HCM diagnostic billing code (ICD-9 or ICD-10), from 2014 to 2019. The class labels HCM yes and HCM no were assigned by manual review of medical records as well as the outcomes of the gold standard imaging tests for HCM diagnosis. The developed model performed well in finding HCM patients with an accuracy of 95%, recall of 99%, and precision of 97%. The F1 score was 98 %, while 4% of patients were misclassified. This model will be translated into clinical practice for a clinical decision support system in EIMS to assist providers in the accurate diagnosis of HCM from echo data automatically while ensuring high-quality echo interpretation.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 Design of Medical Devices Conference, DMD 2021
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791884812
DOIs
StatePublished - 2021
Event2021 Design of Medical Devices Conference, DMD 2021 - Virtual, Online
Duration: Apr 12 2021Apr 15 2021

Publication series

NameProceedings of the 2021 Design of Medical Devices Conference, DMD 2021

Conference

Conference2021 Design of Medical Devices Conference, DMD 2021
CityVirtual, Online
Period4/12/214/15/21

Keywords

  • Decision support system
  • Echocardiography
  • Hypertrophic cardiomyopathy
  • Machine learning
  • Random forest

ASJC Scopus subject areas

  • Biomedical Engineering

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