Current role and future perspectives of artificial intelligence in echocardiography

Table 1 Current applications, strengths, and limitations of artificial intelligence in echocardiography

Applications of AI in echocardiography	Strengths	Limitations
View interpretation and classification	View identification and classification among thousands of images. Possibility of quantification of both structure and function. Possibility of disease diagnosis	Lack of learning process clarification. Possibility of imperfect classification. Image quality is often suboptimal, and nonstructural echocardiographic data need careful preprocessing by the specialist to build the definitive model. Non-standardized intermediate off-axis and continuously rotational and sweeping views, which can be clinically very helpful, even though of low technical quality, are difficult to be managed by AI models
Measuring anatomy and morphofunctional structure	Building a patient similarity model (e.g., for predicting major cardiac events). Comparing automatic analysis between echocardiography and other imaging modalities	Possibility of suboptimal image quality. Possibility of limited number and representativeness of datasets. Current inferiority of automatic compared to semi-automatic software. Frequently inadequate standardization
Wall motion abnormalities detection	Reducing the potential operator-dependent misreading. Detecting different patterns of responses to stress. Possibility of integration with other technologies (e.g., strain technology)	AI algorithms are based on the existing real world datasets, that bring with them the same limits and misclassification risks. Possibility of suboptimal images quality (which implies the exclusion of some acquisitions, hence limited authenticity). Presence of arrhythmias (difficult to be managed by AI models)

AI: Artificial intelligence.