Deep Learning in Echocardiography for Enhanced Detection of Left Ventricular Function and Wall Motion Abnormalities

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Cardiovascular diseases (CVDs) are a major global cause of mortality.
• Early detection and improved diagnostic methods are crucial for better CVD treatment outcomes.
• Echocardiography is a key imaging modality for assessing heart function.

Purpose of the Study:

• To systematically review the integration of deep learning (DL) in echocardiography for cardiovascular abnormality detection.
• To evaluate the role of deep convolutional neural networks (DCNNs) in enhancing echocardiographic diagnostic precision.

Main Methods:

• Systematic review adhering to PRISMA 2020 guidelines.
• Comprehensive literature search across IEEE Xplore, PubMed, and Web of Science.
• Analysis of 29 studies focusing on DL techniques, specifically DCNNs, in echocardiography.

Main Results:

• Deep learning significantly improves the accuracy and reproducibility of detecting and classifying echocardiographic data.
• DL excels in enhancing the measurement of left ventricular function and identifying wall motion abnormalities.
• Identified challenges include data diversity, image quality, and computational demands for DL models.

Conclusions:

• Deep learning holds substantial potential to advance the diagnostic capabilities of echocardiography.
• Successful clinical implementation necessitates overcoming barriers related to data quality, computational resources, and system integration.