Fully automated mouse echocardiography analysis using deep convolutional neural networks
Chong Duan, Mary Kate Montgomery, Xian Chen, Soumya Ullas, John C. Stansfield, Kevin E. McElhanon, Dinesh Hirenallur‐Shanthappa
Abstract
Echocardiography is commonly used in preclinical research to evaluate cardiac structure and function. Despite the broad applications across therapeutic areas, the analysis of echo data is laborious and susceptible to interreader variability. In this study, we developed a fully automated mouse-echocardiography neural net (MENN). Cardiac measurements from MENN showed excellent correlations with manual analysis. Furthermore, the use of MENN leads to >92% reduction in analysis time and potentially eliminates the interobserver variability issue.
Topics & Concepts
Convolutional neural networkComputer scienceArtificial intelligencePattern recognition (psychology)COVID-19 diagnosis using AIECG Monitoring and AnalysisNon-Invasive Vital Sign Monitoring