In the ever-evolving landscape of healthcare, our collaboration with renowned Professor Hatem Zaag represents a milestone in our pursuit of innovative solutions. Our recent research paper, titled “Unsupervised Physics-Informed Neural Network in Reaction-Diffusion Biology Models (Ulcerative Colitis and Crohn’s Disease Cases) – A Preliminary Study,” signifies our commitment to driving change at the intersection of biology, applied mathematics, and artificial intelligence.

The Healthcare Challenge:
Colorectal cancer and inflammatory bowel diseases pose significant health challenges worldwide, with Colorectal cancer being the third most commonly diagnosed cancer globally. These diseases are notorious for being difficult to diagnose, requiring an elevated level of expertise and precision. Early detection is the linchpin for improving the success rates of treatment.

How we approached this challenge:
Our endeavor focuses on the sophisticated analysis of image data to uncover intricate insights into the spatial distribution of these diseases within the expansive realm of the digestive system.
By understanding the interaction between immune cells and bacteria, we lay the foundation for diagnosing disease presence and stage. We then translate these intricate biological mechanisms into mathematical models. These models predict the evolution of bacterial populations within the digestive system.

The Power of AI & Machine Learning:
To tackle the complexity of these diseases, we employ artificial intelligence and machine learning techniques. By harnessing AI to resolve complex differential equations, we navigate the challenging terrain of modeling inflammatory bowel diseases. Our approach unites image analysis with mathematical modeling through Physics-Informed Neural Networks, forging a path to greater comprehension of these diseases and their spatial distribution.