
Peer-reviewed Publications
10+ publications in 2024 alone demonstrate the accuracy and clinical relevance of heart.ai
Comparison of experienced and inexperienced raters using automated deep learning CT analysis to evaluate tricuspid valve and right heart morphology
05 / 2025
Mattig, Isabel et al.
Electrosurgical-laceration and stabilization of two PASCAL devices using artificial intelligence-based procedural planning: a case report
05 / 2025
Nienaber, Stephan et al.
Deep Learning Automation of Complete Heart Segmentation for Planning Multivalvular Structural Heart Interventions
05 / 2025
Khalique, Omar
Investigation of chronic right heart volume overload in severe tricuspid regurgitation based on computed tomographic analyses
04 / 2025
Hülsmann, Benno et al.
Artificial Intelligence-based evaluation of cardiac computed tomography in patients with tricuspid regurgitation and pulmonary hypertension
04 / 2025
Kirchner, Johannes et al.
Optimierung des Screenings für einen katheterbasierten Mitralklappenersatz: Vergleich einer vollautomatischen KI-basierten CT-Analyse versus einer herkömmlichen manuellen Core-Lab Auswertung
04 / 2025
Curio, Jonathan et al.
Predictive value of CT-based and AI-reconstructed 3D-TAPSE in patients undergoing transcatheter tricuspid valve repair
01 / 2025
Kirchner, Johannes et al.
Evolving perspectives on aortic stenosis: the increasing importance of evaluating the right ventricle before aortic valve intervention
01 / 2025
Androshchuk, Vitaliy et al.
Automated deep learning CT analysis of
tricuspid valve and right heart morphology
11 / 2024
Mattig, Isabel et al.
Fully automated CT analysis with a deep-learning based algorithm for pre-procedural planning in transcatheter aortic valve implantation
10 / 2024
Arsalan, Mani et al.
Streamlining TMVR Screening: A Comparative Analysis of Fully Automated AI - Based CT Analysis versus Manual Core - Lab Approach
10 / 2024
Beyer, Martin et al.
A Fully Automated AI-Based Software for Structural Mitral Valve CT-Analysis
10 / 2024
Beyer, Martin et al.
Evolving capabilities of computed tomography imaging for transcatheter valvular heart interventions – new opportunities for precision medicine
09 / 2024
Androschchuk, Vitaliy et al.
Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair
09 / 2024
Kirchner, Johannes et al.
Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis
08 / 2024
Mattig, Isabel et al.
Streamlining TMVR Screening: A Comparative Analysis of Fully Automated AI - Based CT Analysis versus Manual Core - Lab Approach
06 / 2024
Curio, Jonathan et al.
Computed tomography anatomic predictors of outcomes in patients undergoing tricuspid transcatheter edge-to-edge repair
02 / 2024
Bartkowiak, Joanna et al.
Vollautomatische CT-Analyse zur präinterventionellen Planung vor TAVI mittels deep learning Algorithmus: Vergleich zum Goldstandard
02 / 2024
Arsalan, Mani et al.
Analysis of tricuspid annulus dimensions and RCA-proximity with artificial intelligence-based software for procedural planning of percutaneous tricuspid annuloplasty
01 / 2024
Kirchner, Johannes et al.
Predictive value of right ventricular geometry and function evaluated in AI-computed tomography in patients undergoing transcatheter tricuspid valve repair
11 / 2023
Gercek, Muhammed et al.
Measurement of cardiac Volumes in Patients with Tricuspid Regurgitation with CT enhanced Artificial Intelligence in Comparison with cardiac MRI
04 / 2023
Kirchner, Johannes et al.
Technical Aspects and Development of Transcatheter Aortic Valve Implantation
07 / 2022
Steblovnik, Klemens et al.
Right Heart Morphology of Candidate Patients for Transcatheter Tricuspid Valve Interventions
12 / 2021
Khalique, Omar K et al.