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KEY RESPONSIBILITIES:

The primary goal is to explore the usage of machine learning (ML) techniques to analyze large-scale MRI datasets for the extraction of quantitative imaging biomarkers that can predict disease outcomes and improve cardiovascular risk stratification. For example, by using whole-body MRI (wbMRI) to assess organ-specific biological aging, we aim to create standardized and explainable ML models that support early detection of cardiovascular risk. Developing models that perform comparably to state-of-the-art deep learning architectures while improving interpretability and clinical applicability remains a key objective of this analysis.

OBJECTIVES: 

1. Develop ML-based segmentation and biomarker extraction pipelines using large-scale MRI datasets.
   
2. Validate and optimize ML models for robust feature extraction and quantitative model verification, emphasizing explainability and reproducibility.
   
3. Conduct statistical and survival analyses to assess associations between imaging biomarkers and disease outcomes and report the findings to the scientific community.

MILESTONES: 

1. Software Development (1 month): Design and implement automated pipelines for MRI processing, segmentation, and biomarker quantification.
   
2. Model Development and Validation (1 month): Train, optimize, and validate ML models to ensure robustness and reproducibility while reducing manual analysis time by approximately 70%.
   
3. Manuscript and Abstract Preparation (half month): Compile results and submit a research abstract to a major cardiovascular conference.

OUTCOMES:

• Learning new deep learning-based computer vision algorithms applied to medical image segmentation, focusing on UKBioBank Whole Body images initially.
  
• Gaining experience in research writing journals and conference publications.
  
• Developing skills to present research results to the scientific community effectively.

In the next phase, this work will evolve from single-organ analysis to a comprehensive, system-wide understanding of biological aging using the entire 80,000-participant UK Biobank dataset. By developing individualized organ-age models, quantifying systemic aging differences, and standardizing segmentation pipelines, the project will establish a scalable framework for integrating imaging, clinical, and genomic data. These advancements will not only enable high-precision cardiovascular risk prediction but also contribute to a broader understanding of how aging manifests across organ systems. Ultimately, the insights gained will form the foundation for a publishable journal manuscript, support thesis preparation, and advance the long-term goal of translating AI-driven imaging biomarkers into real-world clinical decision-making.

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