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Updated: Jul 26, 2025
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
A data-efficient zero-shot and few-shot Siamese approach for automated diagnosis of left ventricular hypertrophy
Moomal Farhad1, Mohammad Mehedy Masud1, Azam Beg1
1College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates.
Insights
We developed a data-efficient deep learning method for automated Left Ventricular Hypertrophy (LVH) diagnosis from echocardiograms. This novel approach improves accuracy and reliability, addressing challenges in medical data availability for diagnosing this critical heart condition.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Left Ventricular Hypertrophy (LVH) is a serious condition requiring accurate diagnosis via echocardiography.
- Manual interpretation of echocardiograms is time-consuming and prone to diagnostic errors.
- Limited availability of medical data poses a significant challenge for developing automated diagnostic tools.
Purpose of the Study:
- To develop a data-efficient deep learning technique for automated LVH classification using echocardiograms.
- To address the challenge of limited medical data by proposing novel zero-shot and few-shot learning algorithms.
- To improve the diagnostic accuracy and reliability of LVH detection.
Main Methods:
- Collected a novel dataset of normal and LVH echocardiograms from 70 patients.
- Introduced modified Siamese network-based zero-shot and few-shot algorithms for image classification.
- Classification was based on a cutoff distance, eliminating the need for text vectors in zero-shot learning.
Main Results:
- Achieved up to 8% precision improvement for zero-shot learning and 11% for few-shot learning compared to state-of-the-art methods.
- Demonstrated superior performance in automated LVH classification.
- Attained better inter-observer and intra-observer reliability scores than expert echocardiographers.
Conclusions:
- The proposed data-efficient deep learning technique offers a promising solution for automated LVH diagnosis.
- Novel zero-shot and few-shot algorithms effectively address data scarcity in medical imaging.
- The approach enhances diagnostic accuracy and reliability, potentially aiding clinical decision-making.
Abstract:
Left ventricular hypertrophy (LVH) is a life-threatening condition in which the muscle of the left ventricle thickens and enlarges. Echocardiography is a test performed by cardiologists and echocardiographers to diagnose this condition. The manual interpretation of echocardiography tests is time-consuming and prone to errors. To address this issue, we have developed an automated LVH diagnosis technique using deep learning. However, the availability of medical data is a significant challenge due to varying industry standards, privacy laws, and legal constraints. To overcome this challenge, we have proposed a data-efficient technique for automated LVH classification using echocardiography. Firstly, we collected our own dataset of normal and LVH echocardiograms from 70 patients in collaboration with a clinical facility. Secondly, we introduced novel zero-shot and few-shot algorithms based on a modified Siamese network to classify LVH and normal images. Unlike traditional zero-shot learning approaches, our proposed method does not require text vectors, and classification is based on a cutoff distance. Our model demonstrates superior performance compared to state-of-the-art techniques, achieving up to 8% precision improvement for zero-shot learning and up to 11% precision improvement for few-shot learning approaches. Additionally, we assessed the inter-observer and intra-observer reliability scores of our proposed approach against two expert echocardiographers. The results revealed that our approach achieved better inter-observer and intra-observer reliability scores compared to the experts.
