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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

SUSPENDED

Total Enrollment

1664 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-12-20

Study Completion Date

2024-09-01

Brief Summary

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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™

Detailed Description

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Conditions

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DaTSCAN SPECT Scans

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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All patients underwent DaTSCAN SPECT scans

No interventions assigned to this group

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

85 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

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Antoine VERGER

MD, PhD

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Nuclear medicine department CHRU de NANCY

Vandœuvre-lès-Nancy, , France

Site Status

Countries

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France

Other Identifiers

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2021PI187

Identifier Type: -

Identifier Source: org_study_id