Medical Data Analysis Platform

Overview

This project develops advanced AI systems for medical data analysis, focusing on Doppler ultrasound interpretation and dietary intake modeling. The platform addresses the challenges of measurement error and provides robust analysis tools for healthcare applications.

Key Components

• Doppler Ultrasound Analysis: Machine learning models for ultrasound data interpretation
• Dietary Data Modeling: AI modeling of dietary intakes with measurement error handling
• Inverse Problems: Diffusion models for solving medical inverse problems
• Statistical Modeling: Advanced statistical techniques for healthcare data

Technical Innovations

• Diffusion Models: Novel approaches for inverse problems in medical imaging
• Measurement Error Handling: Robust modeling techniques for noisy medical data
• Multi-modal Analysis: Integration of different medical data sources
• Real-time Processing: Efficient algorithms for clinical applications

Collaborations

• Moonrise Medical Inc: Doppler ultrasound data analysis
• Nutrition and Dietary Research Group: University of Waterloo multidisciplinary research
• Medical Imaging Research: Academic and clinical partnerships

Publications

• Nazemi, A., Sepanj, M.H., Pellegrino, N., Czarnecki, C. and Fieguth, P. “Particle-Filtering-based Latent Diffusion for Inverse Problems.” arXiv preprint, 2024.
• Spicker D., Nazemi A., Hutchinson J., Fieguth P., Kirkpatrick S.I., Wallace M., Dodd K.W. “Challenges for Predictive Modeling With Neural Network Techniques Using Error‐Prone Dietary Intake Data.” Statistics in Medicine, 2025.

Impact

This research contributes to improved healthcare diagnostics and patient care through advanced AI techniques, enabling more accurate medical assessments and personalized treatment plans.