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

NCT05080296 · Status: SUSPENDED · Type: OBSERVATIONAL · Enrollment: 1664

Last updated 2023-12-01

No results posted yet for this study

Summary

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™

Conditions

  • DaTSCAN SPECT Scans

Sponsors & Collaborators

  • Central Hospital, Nancy, France

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-12-20
Primary Completion
2023-05-01
Completion
2024-09-01

Countries

  • France

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05080296 on ClinicalTrials.gov