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

Right ventricular size and function are vital to risk stratification in pulmonary hypertension, valvular disease, and congenital heart disease, yet right ventricular assessment remains technically demanding and subject to interpreter variability. This review aims to synthesize the rapidly expanding evidence on the application of artificial intelligence to RV structural and functional assessment across echocardiography, cardiac magnetic resonance (CMR), and computed tomography.

Artificial intelligence has demonstrated accuracy approaching interobserver variability for automated right ventricular segmentation and chamber quantification across imaging modalities. Functional applications include artificial intelligence derived fractional area change, tricuspid annular plane systolic excursion, free-wall strain, and ejection fraction estimation compared against CMR. Emerging applications address right ventricular-pulmonary artery coupling and hemodynamic phenotyping through afterload-aware, physiology-centered models.

Current artificial intelligence tools can standardize and accelerate established right ventricular measurements, with the strongest performance in echocardiographic segmentation, annular tracking, and ejection fraction surrogates. Translation into clinical practice will require robust external validation across disease phenotypes, hybrid artificial intelligence workflows that loop in humans, and prospective studies that display measurable impact on clinical efficiency and patient outcomes.