Heart failure with reduced ejection fraction (HFrEF) can be defined in general terms as a big an‎d most critical disease among cardiovascular disorders. Accordingly, if untreated, it could cause serious morbidity. The stan‎dard against which a diagnosis is made is through transthoracic echocardiography, a modality that depends largely on human interpretation. This very fact can lead to variability in reporting an‎d discrepancies in borderline cases regarding ejection fraction. The seminar, therefore, seeks to compare several recent AI an‎d deep learning methods for HFrEF detection an‎d highlight gaps in the literature; such are the strong single-frame assumptions, very limited temporal modeling, an‎d great sensitivity around clinically important EF thresholds.Heart failure with reduced ejection fraction (HFrEF) can be defined in general terms as a big an‎d most critical disease among cardiovascular disorders. Accordingly, if untreated, it could cause serious morbidity. The stan‎dard against which a diagnosis is made is through transthoracic echocardiography, a modality that depends largely on human interpretation. This very fact can lead to variability in reporting an‎d discrepancies in borderline cases regarding ejection fraction. The seminar, therefore, seeks to compare several recent AI an‎d deep learning methods for HFrEF detection an‎d highlight gaps in the literature; such are the strong single-frame assumptions, very limited temporal modeling, an‎d great sensitivity around clinically important EF thresholds.

10/29/2025 12:00:00 AM

88250

1404/08/11

A Comprehensive Study of AI-Based Heart Failure Classification Using Transthoracic Echocardiography (TTE)

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