This paper is in the following e-collection/theme issue:

e-Cardiology and Telemonitoring in Cardiology (224) Heart Failure Self-Management (180) New Methods (1145) Mobile Apps for Cardiology (190) mHealth for Telemedicine and Homecare (539) Mobile Health (mhealth) (3515) Novel Sensors and Data Acquisition Methods in Cardiology (231) mHealth for Data Collection and Research (1213) Advanced Data Analytics in eHealth (216)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57111, first published .
Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

Authors of this article:

Martin Huecker1 Author Orcid Image ;   Craig Schutzman1 Author Orcid Image ;   Joshua French1 Author Orcid Image ;   Karim El-Kersh2 Author Orcid Image ;   Shahab Ghafghazi1 Author Orcid Image ;   Ravi Desai3 Author Orcid Image ;   Daniel Frick1 Author Orcid Image ;   Jarred Jeremy Thomas1 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Gosser C, Daniel L, Huecker M, Cavallazzi R, Rivas H, Thomas J, Close R. Mobile Phone Auscultation Accurately Diagnoses Chronic Obstructive Pulmonary Disease Using Nonlinear Respiratory Biofluid Dynamics. Diagnostics 2025;15(12):1550 View
  2. Liu Y, Oloko-Oba M, Wood K, Lloyd M, Ho J, Hertzberg V. Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database. JMIR Cardio 2025;9:e77380 View