# Enhanced Clinical Diagnosis through Imaging and Modeling: A Machine Learning Data Fusion Framework

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2021 · $246,645

## Abstract

PROJECT SUMMARY/ABSTRACT
In this proposal, we will use modern machine learning techniques to combine and enhance
computational modeling predictions. We will overcome the physics deficiencies that are
inherent in modeling assumptions by including ground-truth clinical measurements, but in turn
provide predictions that are more informative (higher spatial and temporal resolution) than the
original clinical measurements. Furthermore, we will implement this framework as a surrogate
model that can be used in real time and can replace current models with prohibitively high
computation cost. If successful, the proposed research will enable lab-to-bedside deployment of
a vast array of existing and future computational models and it ultimately could lead to a paradigm
shift in health care workflow.
 Our overarching hypothesis is that the statistical correlations between computational
models and clinical measurements can be exploited in a probabilistic data-fusion framework
for more accurate predictions. Our multi-fidelity framework is based on an autoregressive
Gaussian Process (GP) scheme. Our proposed scheme is a non-parametric Bayesian machine
learning technique that has a probabilistic workflow and estimates uncertainty at different
levels of fidelity in a principled manner.
 As a template for other clinical applications, we will develop this framework for
perfusion scanning of brain hemodynamics in healthy and stroke populations, which has a
significant health application. In Aim 1, we will simulate cerebral perfusion in healthy and
stroke populations based on CT and MR angiography (CTA and MRA) scans. We will simulate and
validate cerebral blood perfusion in healthy and stroke gender-balanced subjects. In Aim 2, we
construct subject-specific multi-fidelity models by combining computational results and
perfusion scans. We propose to leverage the multi-fidelity model to reduce scan time and
radiation exposure by incorporating simulated perfusion maps with CT perfusion scans.

## Key facts

- **NIH application ID:** 10287669
- **Project number:** 1R21EB032187-01
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Hessam Babaee
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $246,645
- **Award type:** 1
- **Project period:** 2021-09-07 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10287669

## Citation

> US National Institutes of Health, RePORTER application 10287669, Enhanced Clinical Diagnosis through Imaging and Modeling: A Machine Learning Data Fusion Framework (1R21EB032187-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10287669. Licensed CC0.

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