Gipsy Urrutia

I started my PhD less than a year ago, and during this time, while working on research aimed at detecting the severity of aortic stenosis, I have gained extensive knowledge—not only about medicine and programming but also about culture and language. Before continuing, let me explain a little about this disease.

The heart is one of the most vital organs in living beings, and aortic stenosis is one of the diseases that can affect it, directly impacting the aortic valve. This valve, along with the other heart valves, ensures that blood flows in one direction. Aortic stenosis causes the valve tissue to stiffen, preventing it from opening and closing properly. When patients experience this condition, the heart begins to sustain damage, some of which may be irreversible, even after surgery.

This disease predominantly affects elderly patients or those with pre-existing conditions. It is a silent disease, meaning that symptoms often appear only after significant heart deterioration, especially in those with a sedentary lifestyle. This makes diagnosis complex, and once symptoms manifest, the disease progresses rapidly. The primary treatment is surgery, in which a prosthetic valve replaces the original, but in many cases, the advanced stage of the disease prevents significant improvement.

Currently, the main criterion for surgery is the presence or absence of symptoms. However, studies indicate that the severity of the disease is not solely determined by valve stiffness.

The aim is to develop imaging- and modeling-based technology to measure local changes in ventricular tissue properties during exercise, considering that symptoms often arise in physically active individuals.

This approach involves creating 3D contractility maps through biomechanical-computational modeling of the heart, personalized using exercise cardiac MR images. In the long term, the goal is to provide clinical decision support for performing aortic valve replacement (AVR) before irreversible cardiac damage occurs.

So far, I have been able to recreate patients’ hearts in 3D models to simulate heart function. To do this, I had to complete several steps: first, studying the MRIs; second, performing segmentation; third, conducting post-processing, where the model is refined; and finally, designing the simulation mesh. The challenge lies in programming the tissue material, which consists of fibers, and simulating heart functions using patient data. I hope to report more progress soon.