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Updated: Jan 16, 2026
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Encoding of Demographic and Anatomical Information in Chest X-Ray-Based Severe Left Ventricular Hypertrophy
Basudha Pal1, Rama Chellappa1,2, Muhammad Umair3,4
1Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Insights
Deep learning models can now detect severe left ventricular hypertrophy (SLVH) from chest X-rays, offering a cost-effective alternative to traditional imaging. This AI framework provides accurate diagnosis and interpretability for clinical use.
Area of Science:
- Artificial Intelligence in Medicine
- Cardiology
- Medical Imaging Analysis
Background:
- Severe left ventricular hypertrophy (SLVH) is a significant risk factor for heart failure.
- Current diagnostic methods like echocardiography and MRI are costly and burdensome.
- There is a need for accessible and efficient SLVH detection methods.
Purpose of the Study:
- To develop and validate a deep learning framework for SLVH classification directly from chest radiographs.
- To evaluate the interpretability of the deep learning model by quantifying attribute encoding.
- To assess the potential of AI in improving cardiac abnormality detection workflows.
Main Methods:
- Utilized a class-balanced subset of the CheXchoNet dataset.
- Fine-tuned a ResNet-18 model and pretrained a Vision Transformer (ViT) encoder.
- Applied Mutual Information Neural Estimation (MINE) to analyze feature interpretability regarding clinical attributes.
Main Results:
- The Vision Transformer (ViT) model achieved an AUROC of 0.82 and AUPRC of 0.80 for SLVH detection.
- MINE analysis demonstrated that the model effectively encodes clinical attributes like age, sex, and cardiac dimensions.
- The model showed strong performance without requiring demographic or intermediate anatomical data.
Conclusions:
- Chest radiographs alone are sufficient for accurate SLVH classification using deep learning.
- The developed framework offers both diagnostic accuracy and quantitative interpretability.
- This AI approach shows promise for enhancing clinical decision support and patient triage systems.
Abstract:
Background. Severe left ventricular hypertrophy (SLVH) is a high-risk structural cardiac abnormality associated with increased risk of heart failure. It is typically assessed using echocardiography or cardiac magnetic resonance imaging, but these modalities are limited by cost, accessibility, and workflow burden. We introduce a deep learning framework that classifies SLVH directly from chest radiographs, without intermediate anatomical estimation models or demographic inputs. A key contribution of this work lies in interpretability. We quantify how clinically relevant attributes are encoded within internal representations, enabling transparent model evaluation and integration into AI-assisted workflows. Methods. We construct class-balanced subsets from the CheXchoNet dataset with equal numbers of SLVH-positive and negative cases while preserving the original train, validation, and test proportions. ResNet-18 is fine-tuned from ImageNet weights, and a Vision Transformer (ViT) encoder is pretrained via masked autoencoding with a trainable classification head. No anatomical or demographic inputs are used during training. We apply Mutual Information Neural Estimation (MINE) to quantify dependence between learned features and five attributes: age, sex, interventricular septal diameter (IVSDd), posterior wall diameter (LVPWDd), and internal diameter (LVIDd). Results. ViT achieves an AUROC of 0.82 [95% CI: 0.78-0.85] and an AUPRC of 0.80 [95% CI: 0.76-0.85], indicating strong performance in SLVH detection from chest radiographs. MINE reveals clinically coherent attribute encoding in learned features: age > sex > IVSDd > LVPWDd > LVIDd. Conclusions. This study shows that SLVH can be accurately classified from chest radiographs alone. The framework combines diagnostic performance with quantitative interpretability, supporting reliable deployment in triage and decision support.
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