We’re excited to launch the sixth edition of the AFRICAI Journal Club, presented by Enenche Oche, part of our ongoing series that brings together researchers, students, and professionals to critically engage with cutting-edge scientific papers at the intersection of AI and healthcare.
📄 Rudolf L.M. van Herten, Amedeo Chiribiri, Marcel Breeuwer, Mitko Veta, Cian M. Scannell et al. (2022)
“Physics-informed neural networks for myocardial perfusion MRI quantification”
Medical Image Analysis
Volume 78, May 2022, 102399
DOI: https://www.sciencedirect.com/science/article/pii/S1361841522000512?via%3Dihub
Why this paper? This paper is important because it demonstrates how physics-informed neural network (PINN) can enhance cardiac MRI diagnosis. By learning the underlying physiology, the PINN reduces reliance on large clinical datasets, which is a major barrier in many low- and middle-income countries (LMICs), where access to high-quality medical imaging and labeled data is often limited. This matters even more because cardiovascular disease is the leading cause of death globally, responsible for nearly one in three deaths worldwide, with over 75% of these occurring in LMICs.
Enenche Oche holds a Bachelor’s degree in Physics from the Federal University of Technology, Minna. He currently works as a Research Analyst at SCIDaR, supporting data-driven health research and innovation. He is an alumnus of the African School of Fundamental Physics and Applications (ASP); his research interests focus on applying Physics and AI to oncology. Beyond research, Enenche is passionate about science communication. He previously served as Director of Research at DUALITY, a student-led physics podcast that simplifies complex scientific ideas.
Event Details
• Date: Thursday, 30 October 2025
• Time: 13:00 GMT | 14:00 WAT | 15:00 CAT |
• Duration: 60 minutes (30-minute talk + 30-minute discussion)
• Location: Online via Zoom (link shared upon registration)
• Register now: link
Full article Reference
Rudolf L.M. van Herten, Amedeo Chiribiri, Marcel Breeuwer, Mitko Veta, Cian M. Scannell et al. (2022). Physics-informed neural networks for myocardial perfusion MRI quantification. Medical Image Analysis Volume 78, May 2022, 102399 DOI: https://www.sciencedirect.com/science/article/pii/S1361841522000512?via%3Dihub
