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Review
. 2021 Mar 30;10(7):1391.
doi: 10.3390/jcm10071391.

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Zeynettin Akkus  1 , Yousof H Aly  1 , Itzhak Z Attia  1 , Francisco Lopez-Jimenez  1 , Adelaide M Arruda-Olson  1 , Patricia A Pellikka  1 , Sorin V Pislaru  1 , Garvan C Kane  1 , Paul A Friedman  1 , Jae K Oh  1
Affiliations
Review

Artificial Intelligence (AI)-Empowered Echocardiography Interpretation: A State-of-the-Art Review

Zeynettin Akkus et al. J Clin Med. .

Abstract

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.

Keywords: artificial intelligence; cardiac ultrasound; echocardiography; portable ultrasound.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Sample US images showing different US modes. (A) B-mode image of the apical 4 chamber view of a heart. (B) Doppler image of mitral inflow. (C) Contrast enhanced ultrasound image of left ventricle. (D) Strain imaging of the left ventricle.
Figure 2
Figure 2
The context of artificial intelligence, machine learning, and deep learning. SVM: Support Vector Machine. CNN: convolutional neural networks, R-CNN: recurrent CNN, ANN: artificial neural networks.
Figure 3
Figure 3
A framework of training a deep-learning model for classification of myocardial diseases. Operations between layers are shown with arrows. SGD: Stochastic Gradient Descent.
Figure 4
Figure 4
The flowchart of systematic review that includes identification, screening, eligibility, and inclusion.
Figure 5
Figure 5
The flowchart of automated artificial-intelligence-empowered echo (AI-Echo) interpretation pipeline using a chain approach. QC: Quality Control.
Figure 6
Figure 6
A schematic diagram of AI (artificial intelligence) interpretation of echocardiography images for preliminary diagnosis and triaging patients in emergency and primary care clinics. POCUS: point of care ultrasound.
See this image and copyright information in PMC

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