# Assessment of Deep Learning Classification Methods for Parkinsonism

> **NIH NIH R41** · AUTOMATED IMAGING DIAGNOSTICS, LLC · 2023 · $272,736

## Abstract

SUMMARY
The growth rate in the number of people diagnosed with Parkinsonism is substantial. Estimates indicate that
from 1990 to 2015 the number of Parkinsonism diagnoses doubled, with more than 6 million people currently
diagnosed. By 2040, there will be between 12-14 million people diagnosed with Parkinsonism. Parkinson’s
disease (PD), multiple system atrophy Parkinsonian variant (MSAp), and progressive supranuclear palsy (PSP)
are neurodegenerative forms of Parkinsonism, which can be difficult to diagnose as they share similar motor and
non-motor features, and they each have an increased chance of developing dementia. In the first five years of a
PD diagnosis, about 58% of PD are misdiagnosed, and of these misdiagnoses about half have either MSA or
PSP. Since PD, MSAp, and PSP require unique treatment plans and different medications, and clinical trials
testing new medications require the correct diagnosis, there is an urgent need for clinic ready diagnostic level
markers for differential diagnosis of PD, MSAp, and PSP. A promising approach to identify different forms of
Parkinsonism is diffusion magnetic resonance imaging (dMRI), as there is no contrast drug, the technique is safe
and is already used clinically in traumatic brain injury and stroke. The data collection takes 6-12 minutes and is
compatible on current 3 Tesla MRI systems worldwide. Based on academic research at University of Florida,
Automated Imaging Diagnostics, LLC is developing a commercial software package using free-water diffusion
imaging as an innovative biomarker to help in the diagnosis of PD, MSAp, and PSP. The software currently
distinguishes PD, MSAp, and PSP with over 90% accuracy, and can achieve this accuracy on different scanner
manufactures. Our next goal in this Phase I project is to further improve the innovation and accuracy of our
software technology by employing deep learning classification algorithms for the diagnosis of Parkinsonism. The
specific aim of this current Phase I project is to substitute and compare the use of our existing Support Vector
Machine (SVM) method with two different Residual Deep Neural Network (ResDNN) architectures for estimating
disease type (PD/MSAp/PSP) through the following two milestones. First, in Milestone 1 we will determine if a
ResDNN method that processes the same feature vector as our SVM solution improves the accuracy for
differentiating a) PD and atypical Parkinsonism (MSAp/PSP) and b) MSAp and PSP by 5%. Second, in Milestone
2, we will determine if a ResDNN method that processes directly the raw input image data (instead of our derived
feature vector) improves the accuracy for differentiating a) PD and atypical Parkinsonism (MSAp/PSP) and b)
MSAp and PSP by 5%. This Phase I project will facilitate our long-term objective of developing a high-precision
diagnostic software that can be used by radiologists for diagnosing different types of Parkinsonism.

## Key facts

- **NIH application ID:** 10695418
- **Project number:** 1R41NS132614-01
- **Recipient organization:** AUTOMATED IMAGING DIAGNOSTICS, LLC
- **Principal Investigator:** Angelos Barmpoutis
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $272,736
- **Award type:** 1
- **Project period:** 2023-06-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10695418

## Citation

> US National Institutes of Health, RePORTER application 10695418, Assessment of Deep Learning Classification Methods for Parkinsonism (1R41NS132614-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10695418. Licensed CC0.

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