Artificial intelligence for right ventricular assessment: current evidence and future directions.

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Right ventricular (RV) assessment is crucial for risk stratification in various heart conditions.
• Current RV assessment methods are technically challenging and prone to variability.
• Artificial intelligence (AI) offers potential solutions for automated RV analysis.

Purpose of the Study:

• To review the emerging applications of AI in assessing RV structure and function.
• To synthesize evidence across echocardiography, cardiac magnetic resonance (CMR), and computed tomography.
• To evaluate AI's accuracy and potential in RV quantification and functional assessment.

Main Methods:

• Systematic review of recent literature on AI in RV assessment.
• Analysis of AI performance in segmentation, chamber quantification, and functional parameter estimation.
• Comparison of AI-derived metrics against established imaging modalities, particularly CMR.

Main Results:

• AI demonstrates high accuracy for automated RV segmentation and quantification, comparable to interobserver variability.
• AI accurately estimates functional parameters like fractional area change and ejection fraction.
• Emerging AI applications focus on RV-pulmonary artery coupling and hemodynamic phenotyping.

Conclusions:

• AI tools can standardize and expedite RV measurements, especially in echocardiography.
• Clinical translation requires external validation across diverse disease phenotypes.
• Future implementation necessitates hybrid AI workflows and studies demonstrating clinical impact.