Evaluation of the Use of Machine Learning Techniques to Classify Neurodegenerative PARKinsonian Syndromes (Artificial Intelligence) (PARKIA)

The diagnosis of Parkinson's disease (PD) relies mainly on clinical observation of the patient, looking for the three characteristic symptoms and sometimes remains a real challenge. Machine Learning (ML) algorithms could help to diagnose PD early and differentiate idiopathic PD from atypical Parkinsonian syndromes.

In this context, the work of Castillo-Barnes' team provided a set of imaging features based on morphological characteristics extracted from DaTSCAN® or Ioflupane (iodine-123-labeled radiopharmaceutical) single-photon emission computed tomography (SPECT) scans to discern healthy participants from participants with Parkinson's disease in a balanced set of SPECTs from the "Parkinson's Progression Markers Initiative" (PPMI) data base.

The team of a study evaluated the classification performance of Parkinson's patients and normal controls when semi-quantitative indicators and shape features obtained on the dopamine transporter (DAT) by Ioflupane (123I-IP) single-photon emission computed tomography (SPECT) are combined as a machine learning (ML) feature.

Artificial Intelligence (AI) based methods can improve diagnostic assessments. Several dopaminergic imaging studies using Artificial have reported accuracy of up to 90% for the diagnosis of PD.

These automated approaches use machine learning methods, based on textural analyses, to (i) differentiate PD and healthy subjects, (ii) differentiate PD and vascular parkinsonism, and (iii) distinguish between different forms of atypical parkinsonism.

A study conducted in 2 centers using a linear support vector machine (SVM) model discriminated patients with PD and healthy subjects with an accuracy of 82.5%.This performance is similar to visual assessment by nuclear physicians A linear SVM model based on voxel values of statistical parametric images was able to differentiate PD from vascular parkinsonism with an accuracy of 90.4%. The Nancy team has extensive experience in the detection of PD in SPECT and SPECT/CT scans with Ioflupane or DaTSCAN™

Study Overview

• Status: Suspended
• Conditions: DaTSCAN SPECT Scans

• Study Type: Observational
• Enrollment (Estimated): 1664

Contacts and Locations

Study Locations

• France
  • Vandoeuvre les Nancy cedex, France, 54511
    • Nuclear medicine department CHRU de NANCY

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 85 years (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

All Patients who performed a DaTSCAN SPECT scan in the nuclear medicine department of the Nancy CHRU between 21/11/2011 and 01/09/2017.

Description

Inclusion Criteria:

• Patients who performed a DaTSCAN SPECT scan in the nuclear medicine department of the Nancy CHRU between 21/11/2011 and 01/09/2017.
• Reviews that took place between 11/21/2011 and 9/1/2017 were repatriated from PACS to the processing consoles.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
All patients underwent DaTSCAN SPECT scans

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of the algorithm	Accuracy of the algorithm implemented for the new data in terms of predicting the type of atypical parkinsonian syndrome.	2 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Comparison of two networks	Comparison of the performance of the semi-supervised network with the supervised network, to recognize the importance of unlabeled data in learning	2 months
Analyze the robustness of the network	Analyze the robustness of the network to different data (data from different gamma camera models)	2 months

Collaborators and Investigators

• Sponsor: Central Hospital, Nancy, France

Study record dates

Study Major Dates

• Study Start (Actual): December 20, 2021
• Primary Completion (Actual): May 1, 2023
• Study Completion (Estimated): September 1, 2024

Study Registration Dates

• First Submitted: September 30, 2021
• First Submitted That Met QC Criteria: October 13, 2021
• First Posted (Actual): October 15, 2021

Study Record Updates

• Last Update Posted (Actual): December 1, 2023
• Last Update Submitted That Met QC Criteria: November 28, 2023
• Last Verified: November 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Basal Ganglia Diseases; Movement Disorders; Parkinsonian Disorders
• Other Study ID Numbers: 2021PI187

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No