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Related Experiment Video

Updated: Oct 4, 2025

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation

Published on: October 20, 2016

Automated In-Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic

Hui Xue¹, Jessica Artico^2,3, Rhodri H Davies²

¹National Heart, Lung, and Blood InstituteNational Institutes of Health Bethesda MD.

Journal of the American Heart Association

|February 8, 2022

Summary

An artificial intelligence tool offers automated, precise measurements of global longitudinal shortening and mitral annular plane systolic excursion in cardiac MRI scans. This AI solution provides highly reproducible results and stronger prognostic value for adverse events than manual methods.

Keywords:

artificial intelligence cardiac magnetic resonance imaging global longitudinal shortening, reproducibility image processing prognosis

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A Simplified Stepwise Approach to Echo Guidance during Percutaneous Mitral Valve Repair

A Simplified Stepwise Approach to Echo Guidance during Percutaneous Mitral Valve Repair

Published on: October 16, 2021

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Last Updated: Oct 4, 2025

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Published on: October 20, 2016

High-frequency High-resolution Echocardiography: First Evidence on Non-invasive Repeated Measure of Myocardial Strain, Contractility, and Mitral Regurgitation in the Ischemia-reperfused Murine Heart

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Published on: July 9, 2010

A Simplified Stepwise Approach to Echo Guidance during Percutaneous Mitral Valve Repair

A Simplified Stepwise Approach to Echo Guidance during Percutaneous Mitral Valve Repair

Published on: October 16, 2021

Area of Science:

Cardiology
Artificial Intelligence in Medicine
Medical Imaging Analysis

Background:

Global longitudinal shortening (GL-Shortening) and mitral annular plane systolic excursion (MAPSE) are key indicators in heart failure.
Current manual measurement methods are subjective and time-consuming.
Automated analysis using artificial intelligence (AI) is clinically significant for improving accuracy and efficiency.

Purpose of the Study:

To develop and validate an automated AI solution for in-line measurement of GL-Shortening and MAPSE using cardiac magnetic resonance (CMR) imaging.
To assess the reproducibility and prognostic value of AI-derived GL-Shortening and MAPSE compared to manual measurements and ejection fraction.

Main Methods:

An AI model was implemented for automated, in-line processing on CMR scanners.
Reproducibility was assessed using a scan-rescan dataset (n=160).
Prognostic association with adverse events (death or heart failure hospitalization) was evaluated in a large cohort (n=1572).

Main Results:

Automated processing was rapid (≈1.1 seconds per case).
AI measurements demonstrated superior precision over human experts (GL-Shortening: 7.2% vs. 11.1%; MAPSE: 6.5% vs. 9.1%).
AI MAPSE showed the strongest association with adverse outcomes (HR, 2.5), outperforming AI GL-Shortening, manual strain, and LVEF.

Conclusions:

Automated, in-line AI measurement of MAPSE and GL-Shortening provides immediate, highly reproducible results during CMR.
These AI-derived metrics offer significant prognostic value for adverse outcomes in heart failure patients.
The AI solution enhances diagnostic capabilities beyond traditional measures like global longitudinal strain and ejection fraction.