# High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning arXiv: https://arxiv.org/abs/2106.12511 ยท HTML: https://mlsanity.com/paper/2106.12511/ ## Key facts - Cited by 37 papers in the map (data through 14 Oct 2026). - Builds on 3 of its 40 references that are in the map. - First posted on arXiv 23 Jun 2021. ## Builds on (most cited first, 3 of 3) - [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017) - [Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning](https://mlsanity.com/paper/1708.09843/) (2017) - [Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data](https://mlsanity.com/paper/1904.07032/) (2019) ## Built on by (most cited first, 30 of 37) - [Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging](https://mlsanity.com/paper/2308.05764/) (2023) - [Deep Learning Discovery of Demographic Biomarkers in Echocardiography](https://mlsanity.com/paper/2207.06421/) (2022) - [EchoApex: A General-Purpose Vision Foundation Model for Echocardiography](https://mlsanity.com/paper/2410.11092/) (2024) - [GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis](https://mlsanity.com/paper/2308.13217/) (2023) - [Multimodal Foundation Models For Echocardiogram Interpretation](https://mlsanity.com/paper/2308.15670/) (2023) - [Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation](https://mlsanity.com/paper/2506.11777/) (2025) - [Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis](https://mlsanity.com/paper/2207.11581/) (2022) - [MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance](https://mlsanity.com/paper/2501.02427/) (2025) - [EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms](https://mlsanity.com/paper/2307.12229/) (2023) - [DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation](https://mlsanity.com/paper/2503.22257/) (2025) - [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025) - [US-JEPA: A Joint Embedding Predictive Architecture for Ultrasound](https://mlsanity.com/paper/2602.19322/) (2026) - [Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction](https://mlsanity.com/paper/2311.12582/) (2023) - [A Deep Learning-Driven Pipeline for Differentiating Hypertrophic Cardiomyopathy from Cardiac Amyloidosis Using 2D Multi-View Echocardiography](https://mlsanity.com/paper/2404.16522/) (2024) - [Blind Restoration of High-Resolution Ultrasound Video](https://mlsanity.com/paper/2505.13915/) (2025) - [EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode](https://mlsanity.com/paper/2506.22063/) (2025) - [WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements](https://mlsanity.com/paper/2508.12023/) (2025) - [Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing](https://mlsanity.com/paper/2508.17326/) (2025) - [CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab?](https://mlsanity.com/paper/2510.00520/) (2025) - [MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection](https://mlsanity.com/paper/2511.04255/) (2025) - [Automated Interpretable 2D Video Extraction from 3D Echocardiography](https://mlsanity.com/paper/2511.15946/) (2025) - [EchoVLM: Measurement-Grounded Multimodal Learning for Echocardiography](https://mlsanity.com/paper/2512.12107/) (2025) - [A texture-based framework for foundational ultrasound models](https://mlsanity.com/paper/2602.01444/) (2026) - [FrameONE: Hierarchical Motion Modeling for Universal Multi-View Echocardiographic Keyframe Detection](https://mlsanity.com/paper/2607.00748/) (2026) - [ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function](https://mlsanity.com/paper/2312.08567/) (2023) - [Temporally Consistent Mitral Annulus Measurements from Sparse Annotations in Echocardiographic Videos](https://mlsanity.com/paper/2503.12087/) (2025) - [DUSTrack: Semi-automated point tracking in ultrasound videos](https://mlsanity.com/paper/2507.14368/) (2025) - [A DyL-Unet framework based on dynamic learning for Temporally Consistent Echocardiographic Segmentation](https://mlsanity.com/paper/2509.19052/) (2025) - [Subgroup Validity in Machine Learning for Echocardiogram Data](https://mlsanity.com/paper/2512.00976/) (2025) - [Imaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics](https://mlsanity.com/paper/2601.07871/) (2026) ## Abstract (arXiv) Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos.