This research aims to explore the progression, early detection, and biomarker identification of cardiovascular and neuropsychiatric diseases, including heart failure, cardiomyopathy, atrial fibrillation, myocardial infarction, and Alzheimer’s disease. By leveraging clinical and imaging data, the key objectives are:
Predict Disease Progression: To develop AI-driven models for predicting the progression of these diseases, aiding in better forecasting of patient outcomes and guiding clinical decision-making.
Identify Biomarkers: To identify biomarkers that reflect the early stages or progression of these diseases, enabling earlier diagnosis and more personalized treatment strategies.
Early Screening: To design screening tools that detect high-risk individuals or early-stage diseases, facilitating timely intervention and improving patient prognosis.
Examine Disease Interactions: To investigate how cardiovascular and neuropsychiatric diseases, along with their comorbidities, interact and influence each other, potentially guiding more effective treatment approaches.
Scientific Rationale:
Cardiovascular and neuropsychiatric diseases are major contributors to global morbidity and mortality, with complex progression patterns that are often difficult to predict. Early detection and personalized treatment strategies are key to improving patient outcomes. Clinical and imaging data, combined with AI, offer significant potential to uncover hidden patterns in disease progression, identify early biomarkers, and enable more timely interventions. AI models can automate the analysis of multidimensional data, improving prediction accuracy and facilitating early diagnosis. Understanding disease interactions, particularly with comorbidities, is also critical for optimizing treatment. This research aims to leverage AI to advance early detection, disease progression prediction, and biomarker identification, ultimately improving clinical care and disease management.
