Approved Research

Developing Heart Failure Prognostic Model with Multimodal AI: Incorporating Comorbidities, Medical Imaging, and Genetic Data

Lay summary

Aim: Our project focuses on improving the way we diagnose and predict heart failure, a serious heart condition. We aim to do this by using advanced artificial intelligence (AI) technology that considers various aspects of a patient's health.

Scientific Rationale: Currently, heart failure diagnosis relies heavily on one measure, called LVEF, which doesn't always provide a complete picture. We believe that by looking at more factors, such as genetics, lifestyle, and medical imaging, we can create a more accurate and personalized way to diagnose and predict heart failure. This will allow doctors to tailor treatments more effectively.

Project Duration: This research will take several years to complete. We will gather and analyze data from a large group of patients, including their medical history, genetic information, and various medical images. We will use AI to process this data and develop a tool that can help doctors make better decisions when it comes to heart failure.

Public Health Impact: Heart failure is a widespread and life-threatening condition. Our research has the potential to revolutionize how we approach heart failure diagnosis and treatment. By providing doctors with a more precise and personalized tool, we can improve patient outcomes, reduce healthcare costs, and ultimately save lives. This project aligns with the public's interest in better healthcare and has the potential to benefit a large number of people worldwide.