AI in Echocardiography: State-of-the-art Automated Measurement Techniques and Clinical Applications

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  Figure 1.. Illustration of the AI support in echocardiography workflows View Classification―AI identifies and labels the different views of the echocardiographic images. View Classification―AI classifies the images into specific types of echocardiographic views, such as apical 4-chamber (4ch). Auto Measurements―AI performs automatic measurements of cardiac structures, such as left ventricular end-diastolic volume (LVEDV). Echo AI Report―AI generates comprehensive echocardiographic reports based on automated measurements and analyses. AI Diagnosis―AI assists in diagnosing cardiac conditions by analyzing the echocardiographic data.

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  Figure 2.. Comparison between conventional and automated strain measurements in echocardiography Conventional measurement involves manual steps like chamber selection and ROI modification (left). Automated measurement (right) requires no manual intervention and provides instant results. ROI: region of interest.

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  Figure 3.. Impact of the image quality on the measurement time in echocardiography The left side shows examples of good-quality images compared to fair- or poor-quality images. The right-side graph demonstrates that fair- or poor-quality images require significantly more time for measurements (p < 0.01) compared to good-quality images, while reporting time remains consistent (ns).

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  Figure 4.. Erroneous automated measurement case of the posterior wall using AI Inclusion of chordae tendineae in the measurement results in a wall thickness overestimation (16.4 mm), potentially leading to a misdiagnosis of concentric hypertrophy. This case highlights the necessity for an accurate anatomical identification in AI-driven echocardiography.

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  Figure 5.. Strategies for the effective use of AI in echocardiography Inexperienced operators should not use AI alone, while novices are advised to use AI with a mentor. Manual measurements are better with a poor image quality or a neglected imaging. Once these challenges are addressed, high-precision and reliable assessments, improved time efficiency, and reduced workload and patient waiting times are achieved by integrating AI as a collaborative tool.