Development of Deep Learning Model for Biological Age Estimation Using multimodal clinical Data

Biological age is a more accurate indicator of an individual’s health condition compared to chronological age, as it reflects physiological and functional status. This project proposes a deep learning-based system for biological age estimation using multimodal clinical data such as blood biomarkers, medical imaging, lifestyle factors, and physiological signals. The system integrates heterogeneous data sources and applies advanced deep learning models to learn complex relationships between clinical parameters and biological aging. By leveraging multimodal data fusion techniques, the model improves prediction accuracy and provides personalized health insights. The proposed system enables early detection of aging-related risks, supports preventive healthcare, and assists clinicians in making data-driven decisions.