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Updated: Jun 18, 2025
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review
Lies Dina Liastuti1,2, Bambang Budi Siswanto1, Renan Sukmawan1
1Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, 15810 Jakarta, Indonesia.
Insights
Artificial intelligence (AI) shows promise in improving the accuracy and speed of diagnosing heart failure using echocardiography. AI serves as a valuable tool to assist clinicians, but does not replace their essential role in patient care.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Heart failure presents a significant healthcare challenge, particularly in Asia.
- Echocardiography is vital for assessing cardiac function, but faces resource and time constraints, exacerbated by the COVID-19 pandemic.
- Artificial intelligence (AI) offers potential for accurate and rapid heart failure diagnosis.
Purpose of the Study:
- To systematically review the literature on the application of AI in echocardiography for heart failure diagnosis.
- To assess the accuracy and methodologies of AI-driven diagnostic tools.
Main Methods:
- Systematic literature search across multiple databases (Europe PMC, ProQuest, Science Direct, PubMed, IEEE) adhering to PRISMA guidelines.
- Quality and risk of bias assessment of included studies using QUADAS-2.
- Analysis of 14 selected studies out of 2105 retrieved.
Main Results:
- Fourteen studies were included, with five showing risks of bias.
- Commonly used echocardiography views were apical four-chamber (A4C) and apical two-chamber (A2C) from 2D and 3D datasets.
- Convolutional neural networks were the most frequent AI method, with diagnostic accuracy ranging from 57% to 99.3%.
Conclusions:
- AI applications in echocardiography demonstrate potential for enhanced and expedited diagnosis of left heart failure.
- AI should be viewed as a complementary tool to support clinicians, not replace them.
- Clinician involvement remains indispensable for accurate diagnosis and comprehensive patient management.
Background:
Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly.
Methods:
We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies).
Results:
A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%.
Conclusions:
To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care; thus, AI only serves as complementary assistance for clinicians.
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