Journal of Acute Care
Volume 2 | Issue 3 | Year 2023

Artificial Intelligence in Echocardiography: A Revolution in Cardiovascular Imaging

Muralidhar Kanchi1https://orcid.org/0000-0003-3347-0204

Department of Anesthesia, Narayana Institute of Cardiac Sciences, Bengaluru, Karnataka, India

Corresponding Author: Muralidhar Kanchi, Department of Anesthesia, Narayana Institute of Cardiac Sciences, Bengaluru, Karnataka, India, Phone: +91 9980163108; 080 27836966, e-mail: kanchirulestheworld@gmail.com

How to cite this article: Kanchi M. Artificial Intelligence in Echocardiography: A Revolution in Cardiovascular Imaging. J Acute Care 2023;2(3):99–100.

Source of support: Nil

Conflict of interest: None

Keywords: Artificial intelligence, Cardiomyopathies, Cardiovascular diseases, Coronary artery disease, Delivery of healthcare, Echocardiography, Emergency medicine, Heart valve diseases, Observer variation, Ultrasonic waves.


Echocardiography is a noninvasive imaging technique that utilizes ultrasound waves to visualize the heart’s structure and function. Echocardiography plays a vital role in diagnosing and monitoring cardiovascular diseases. The use of echocardiography in critical care and emergency medicine has made a significant advancement in patient care concerning the rapidity of diagnosis, decision-making, and monitoring therapy. With the introduction of artificial intelligence (AI), the field of echocardiography has experienced a tremendous transformation. AI-powered algorithms are enhancing the accuracy, efficiency, and accessibility of echocardiography, offering profound benefits to patients and healthcare professionals alike. The application and use of AI in echocardiography have expanded rapidly to improve consistency and reduce interobserver and intra-observer variability.1 The justification behind the use of AI in echocardiography to recognize cardiac disease is founded on AI’s capacity to analyze features automatically from pictures to data that are beyond human perception.2 A large amount of diagnostic data that is generated during routine echocardiographic examination may go unutilized since all such data may not be interpreted by human experts in a short time.3 AI can help identify the true value of these undiscovered findings and can analyze this information faster than human experts. Therefore, the potential clinical applications of AI in echocardiography are rapidly increasing, including the identification of specific disease states and processes, such as valvular heart diseases, coronary artery disease, hypertrophic cardiomyopathy, cardiac amyloidosis, cardiomyopathies, and cardiac masses.


Image Analysis and Interpretation

The AI algorithms excel in analyzing echocardiographic images. AI can detect and quantify a wide range of cardiac abnormalities, including structural defects, wall motion abnormalities, and valvular pathologies. AI-driven image analysis provides precise measurements and detailed visualizations that aid in diagnosis and treatment planning.

Automated Measurements

Artificial intelligence (AI) can automate the time-consuming and labor-intensive process of measuring key cardiac parameters, such as ejection fraction, left ventricular (LV) volume, and wall thickness. In a recent publication, the automated border detection technique was reproducible and comparable to manual tracings of endocardial contours concerning the calculation of 2D ejection fraction, LV volumes, and global longitudinal strain.4 Using hand-held ultrasound devices equipped with AI hospitalized patients suffering from coronavirus disease 2019 (COVID-19), an agreement with conventional methods was demonstrated.5 AI-guided assessment of left ventricular outflow tract velocity time integral (LVOT-VTI), inferior vena cava collapsibility index (IVC-CI), and B-lines counting were reliable and consistent with manual assessment in COVID-19 patients even by relative novices.6 AI-aided techniques not only reduce the workload of sonographers and cardiologists but also minimize human error.

Image Enhancement

Artificial intelligence (AI) can enhance echocardiographic images by reducing noise, improving contrast, and clarifying details. This results in higher image quality and better diagnostic accuracy, especially in challenging cases.

Workflow Optimization

The AI-driven systems can streamline the echocardiography workflow by automatically organizing and prioritizing studies, making it easier for healthcare professionals to manage their caseload.

Quality Assurance

Artificial intelligence (AI) can serve as a quality control tool, flagging studies that may need further review due to suboptimal image quality or technical issues.

Decision Support

Artificial intelligence (AI) can offer decision support to clinicians, providing real-time feedback and recommendations during echocardiography interpretation. This can assist less experienced practitioners and ensure consistency in diagnosis.


Improved Accuracy

The AI algorithms consistently provide accurate and objective measurements, reducing the risk of human error and misinterpretation.

Time Savings

The AI-driven automation of measurements and image enhancement tasks significantly accelerates the echocardiography process, allowing more patients to be seen in less time.

Access to Expertise

Artificial intelligence (AI) allows for remote interpretation of echocardiography, enabling access to expert opinions regardless of geographical location. Telemedicine applications of AI in echocardiography are particularly valuable.

Enhanced Diagnostics

Artificial intelligence (AI) augments the diagnostic capabilities of healthcare providers, enabling early detection of cardiovascular diseases and precise monitoring of disease progression.


Data Quality

The accuracy of AI models in echocardiography depends on the quality and diversity of the training data. Ensuring high-quality representative datasets is essential.

Regulatory and Ethical Issues

The use of AI in healthcare, including echocardiography, involves ethical concerns about privacy, consent, and decision-making. Regulatory frameworks must evolve to address these issues.


The AI models often function as black boxes, making it challenging for healthcare professionals to understand and trust the recommendations. Developing interpretable AI is an ongoing challenge.

Integration with Clinical Workflow

Implementing AI seamlessly into clinical practice requires overcoming technical, interoperability, and adoption challenges.


Artificial intelligence (AI) in echocardiography is transforming cardiovascular imaging, offering numerous benefits in terms of accuracy, efficiency, and accessibility. From automated measurements and image enhancement to decision support and telemedicine applications, AI is enhancing the capabilities of healthcare providers while improving patient care. However, addressing challenges related to data quality, regulation, and interpretability is crucial to fully realize the potential of AI in echocardiography. As AI technology continues to evolve, its role in cardiac imaging is expected to become increasingly prominent, contributing to better patient outcomes and more efficient healthcare delivery in the field of cardiology.


Muralidhar Kanchi https://orcid.org/0000-0003-3347-0204


1. Sehly A, Jaltotage B, He A, et al. Artificial intelligence in echocardiography: the time is now. RCM 2022;23(8):256. DOI: 10.31083/j.rcm2308256

2. Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart 2022;108(20):1592–1599. DOI: 10.1136/heartjnl-2021-319725

3. Yoon YE, Kim S, Chang HJ. Artificial intelligence and echocardiography. J Cardiovasc Imag 2021;29(3):193–204. DOI: 10.4250/jcvi.2021.0039

4. Knackstedt C, Bekkers SC, Schummers G, et al. Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study. J Am Coll Cardiol 2015;66(13):1456–1466. DOI: 10.1016/j.jacc.2015.07.052

5. Maheshwarappa HM, Mishra S, Kulkarni AV, et al. Use of handheld ultrasound device with artificial intelligence for evaluation of cardiorespiratory system in COVID-19. Indian J Crit Care Med 2021;25(5):524–527. DOI: 10.5005/jp-journals-10071-23803

6. Damodaran S, Kulkarni AV, Gunaseelan V, et al. Automated versus manual B-lines counting, left ventricular outflow tract velocity time integral and inferior vena cava collapsibility index in COVID-19 patients. Indian J Anaesth 2022;66(5):368–374. DOI: 10.4103/ija.ija_1008_21

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