Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.

Skip to main content
Fig. 2 | Echo Research & Practice

Fig. 2

From: Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance

Fig. 2

Linear regression plots comparing the ML model to the reference standards. The intraclass correlation coefficient (ICC) for ML model LVEF and level III echocardiographer LVEF was 0.772 [0.501,1.000] and 0.778 [0.578,1.000] for randomized single videos by visual estimate and segmentation, respectively. The ICC for single video ML model LVEF and level III echocardiographer LVEF was 0.794 [0.173, 1.000] for visual assessment and 0.843 [0.310, 1.000] by segmentation when the expert was able to review all clips for a participant. The ICC for ML model LVEF and derived reported LVEF was 0.798 [0.143, 1.000]

Back to article page