Kirchner, Johannes et al. “Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair” International Journal of Cardiology. (2024) Sep 15;411:132233. doi: 10.1016/j.ijcard.2024.132233. |Read it here

Mattig, Isabel et al. “Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis” Scientific Reports. (2024) Aug 27;14(1):19946. doi: 10.1038/s41598-024-70768-x |Read it here

Bartkowiak, J et al. “Computed tomography anatomic predictors of outcomes in patients undergoing tricuspid transcatheter edge-to-edge repair” Journal of Cardiovascular Computed Tomography (2024). doi.org/10.1016/j.jcct.2024.02.001. | Read it here

Kirchner, J et al. "Analysis of tricuspid annulus dimensions and RCA-proximity with artificial intelligence-based software for procedural planning of percutaneous tricuspid annuloplasty" | J Cardiovasc Comput Tomogr. (2024) Jan 29:S1934-5925(24)00016-9. doi: 10.1016/j.jcct.2024.01.005. | Read it here

Gercek, M et al. “Predictive value of right ventricular geometry and function evaluated in AI-computed tomography in patients undergoing transcatheter tricuspid valve repair.” European Heart Journal vol. 44,2 (2023): 573-589. doi.org/10.1093/eurheartj/ehad655.137| Read it here

London Valves 2023 Presentation: Dr. Isabel Mattig (Charité Berlin) presents on automated CT analysis to predict post-interventional reduction of tricuspid regurgitation | Read it here

ESC Congress 2023 Presentation: Dr. Muhammed Gerçek (Bad Oeynhausen) presents on the predictive value of LARALAB's AI-based chamber quantification for TTVR | Read it here

Khalique, Omar K et al. “Right Heart Morphology of Candidate Patients for Transcatheter Tricuspid Valve Interventions.” Cardiovascular engineering and technology vol. 13,4 (2022): 573-589. doi:10.1007/s13239-021-00595-y | Read it here