Why the study?
Machine learning algorithms and deep learning models are expanding the boundaries of echocardiography, prompting a need to review existing and upcoming AI integration modalities.
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Design
Review
AI may aid automated biventricular echo assessment; leaves open prospective validation before clinical adoption.
Artificial intelligence (AI) has been expanding exponentially in the field of health care. AI not only simplifies disease interpretation but also improves the efficiency of patient management. The novel machine learning algorithms, and deep learning models are expanding the boundaries in the arena of echocardiography. The automated assessment of the biventricular function, atrio-ventricular coupling, Integrating novel approaches like speckle tracking may aid in the identification, classification, diagnosis, and prognostication of cardiovascular abnormalities. Moreover, AI integration reduces the time of interpretation, inter as well as intra-observer variability and provides a rapid, non-invasive and accurate result. AI stands at the pinnacle of echocardiography. Therefore, the index article aims to review the existing and upcoming AI integration modalities in echocardiography with regard to the technique, advantages, limitations, and its clinical application.
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Devishree Das (2025) studied this question.
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