Key result
AI/ML improves echocardiography diagnostic performance by addressing variance during image acquisition and interpretation.
Why the study?
Echocardiography interpretation is burdened by high dependence on operator experience and interobserver variability, which artificial intelligence technologies may help address through automated and consistent interpretations.
AI technologies offer new opportunities to produce accurate, automated, and more consistent echocardiographic interpretations, reducing the modality's high dependence on operator experience.
May improve echocardiography consistency in practice; leaves open need for prospective trials before adoption.
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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Barry et al. (2023) conducted a review in Cardiovascular disease. Artificial intelligence was evaluated. Artificial intelligence and machine learning can improve the diagnostic performance of echocardiography by addressing variance during image acquisition and interpretation.
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