March 3, 2026Open Access

Evaluation of diastolic function: machine learning improves classification of left ventricular filling pressure

Key Points

Improved classification of left ventricular filling pressure leads to better evaluation of heart function and health.
Key parameters previously underused were found to be instrumental in assessing left ventricular filling pressure.

Structured PICO

Does machine learning improve the classification of left ventricular filling pressure?

Intervention

Machine learning (ML) for evaluating left ventricular filling pressure (LVFP)

Outcome

Classification and feasibility of left ventricular filling pressure (LVFP)surrogate

Machine learning improves the feasibility and classification of left ventricular filling pressure, highlighting the value of previously underutilized parameters.

Abstract

ML can improve classification of LVFP, particularly with a higher feasibility. The study unveiled less used parameters as some of the most valuable for evaluating LVFP.

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Discussion

Authors

Faraz H Khan

Katsuji Inoue

N. Ohte

Journals

European Heart Journal - Cardiovascular Imaging

Actions

Institutions

Cleveland Clinic

Yonsei University

Oslo University Hospital

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Evaluation of diastolic function: machine learning improves classification of left ventricular filling pressure

Key Points

Structured PICO

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

Cite this study

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