Development of novel machine learning model for right ventricular quantification on echocardiography—A multimodality validation study

Autor: Ines Sherifi, Jonathan W. Weinsaft, Jiwon Kim, Brian Yum, Ashley Beecy, Mukund Das, Alex Bratt, Richard B. Devereux, Razia Sultana
Rok vydání: 2020
Předmět:
Validation study
Heart Ventricles
Ventricular Dysfunction
Right
Population
Automated segmentation
Original Investigations
Magnetic Resonance Imaging
Cine
right ventricle
030204 cardiovascular system & hematology
Machine learning
computer.software_genre
Best Paper
Machine Learning
03 medical and health sciences
0302 clinical medicine
Humans
Medicine
Radiology
Nuclear Medicine and imaging
cardiovascular diseases
030212 general & internal medicine
education
Original Investigation
Reproducibility
education.field_of_study
business.industry
Reproducibility of Results
Predictive value
right ventricular function
Echocardiography
Rv function
Ventricular Function
Right
cardiovascular system
Manual segmentation
Artificial intelligence
Cardiology and Cardiovascular Medicine
Cardiac magnetic resonance
business
computer
Zdroj: Echocardiography (Mount Kisco, N.y.)
ISSN: 1540-8175
0742-2822
Popis: Purpose Echocardiography (echo) is widely used for right ventricular (RV) assessment. Current techniques for RV evaluation require additional imaging and manual analysis; machine learning (ML) approaches have the potential to provide efficient, fully automated quantification of RV function. Methods An automated ML model was developed to track the tricuspid annulus on echo using a convolutional neural network approach. The model was trained using 7791 image frames, and automated linear and circumferential indices quantifying annular displacement were generated. Automated indices were compared to an independent reference of cardiac magnetic resonance (CMR) defined RV dysfunction (RVEF
Databáze: OpenAIRE
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