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Development of Computational Pipeline for Right Ventricular Hemodynamic Single-Beat Analysis.
Medrxiv : the Preprint Server for Health Sciences
|June 12, 2025
Summary
A new AI tool estimates right ventricular (RV) dysfunction indices from RHC screenshots, correlating with standard methods and predicting patient outcomes in pulmonary hypertension (PH). This advances RV assessment for better PH prognosis.
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Pulmonary Hypertension Research
Background:
- Load-independent indices of right ventricular (RV) dysfunction are crucial for pulmonary hypertension (PH) prognosis but difficult to obtain.
- Current methods for assessing RV function require complex equipment and procedures.
- There is a need for simpler, more accessible methods to evaluate RV performance in PH patients.
Purpose of the Study:
- To develop a novel computer vision artificial intelligence (AI)-based pipeline.
- To estimate load-independent RV functional indices using standard right heart catheterization (RHC) screenshots of the RV pressure-time waveform.
- To validate the pipeline's accuracy and prognostic value in PH patients.
Main Methods:
- A prospective study collected data from 76 PH patients across three centers.
- A MATLAB-based pipeline was developed for image processing and single-beat analysis to predict RV pressure-volume loops and extract RV indices.
- Validation involved Bland-Altman analysis, concordance correlation coefficient (CCC), and Pearson correlation, alongside survival and cluster analyses.
Main Results:
- The AI pipeline demonstrated strong concordance with gold-standard methods for key indices like end-systolic elastance (Ees) and effective arterial elastance (Ea).
- Pipeline-derived Ea and Ees/Ea ratio were significant predictors of clinical outcomes in prognostic analyses.
- Cluster analysis identified two RV sub-phenotypes with distinct hemodynamic profiles, correlating with different prognoses.
Conclusions:
- A novel computational pipeline effectively digitizes RV pressure waveforms to generate single-beat estimates of RV-pulmonary arterial coupling.
- The tool's output shows strong correlation with established single-beat methods.
- This AI-driven approach offers a promising method for assessing RV function and predicting outcomes in pulmonary hypertension.
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