Imperial College London (Francis, Shun-Shin Lab) / Unity UK Echocardiography AI Collaborative
Open, transparent deep-learning method for measuring left ventricular global longitudinal strain (GLS) from routine 2D echocardiography, built as an alternative to proprietary vendor strain software. Unity-GLS is a multi-image neural network (based on the HigherHRNet-W32 pose-estimation architecture) that identifies the mitral annulus, LV apex, and endocardial curve from a target frame plus six neighbouring frames, across apical 4-, 3-, and 2-chamber views. Validated against multi-expert (11-reader) consensus tracings from 100 echocardiograms in a UK-wide collaborative, Unity-GLS agreed with expert consensus as strongly as individual human experts and two proprietary vendor packages (correlation with consensus: 0.91 vs. 0.73-0.85 for other methods).
Architecture
CNN (2D)
Multi-image 2D CNN based on the HigherHRNet-W32 pose-estimation architecture, taking a target echo frame plus 6 neighbouring frames (offsets -9,-3,-1,+1,+3,+9) as input to localize the mitral annulus, LV apex, and endocardial curve for GLS calculation
Added to catalog
2026-08-13
CC BY 4.0
License for model weights only. Associated code may be licensed seperately, check code source for specific terms.
Global longitudinal strain (GLS) of the left ventricle, from apical 4-, 3-, and 2-chamber echo views