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NCT Number: NCT07392411

Study on Artificial Intelligence-Based Facial and Speech-Related Patterns in Parkinson's Disease and Their Digital Biomarkers

This research employs AI to analyze facial expressions and speech patterns, aiming to develop new digital tools for diagnosing and differentiating Parkinson's disease and similar disorders.

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Key information

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Inclusion Criteria for Parkinson's Disease (PD) Group: (1) Diagnostic Criteria: Meet the diagnostic criteria for Parkinson's disease established by the Movement Disorder Society (MDS) in 2015. (2) Age Range: 18-75 years old. (3) Disease Severity: Early-stage PD: Hoehn-Yahr score ≤ 2.5 points. Mid-to-late-stage PD: Hoehn-Yahr score 2.5-5 points. (4) Consent for Data Collection: Willing to undergo facial expression video and speech audio recording. (5) Informed Consent: Signed informed consent form.
  • Inclusion Criteria for Parkinson's Plus Syndromes (MSA-P, PSP) Group: (1) Age Range: 18-75 years old. (2) Diagnostic Criteria: MSA patients must meet the diagnostic criteria established by the [Chinese Expert Consensus on the Diagnosis of Multiple System Atrophy, 2017]. PSP patients must meet the diagnostic criteria established by the [Chinese Clinical Diagnostic Criteria for Progressive Supranuclear Palsy, 2016 Edition]. (3) Consent for Data Collection: Willing to undergo facial expression video and speech audio recording. (4) Informed Consent: Signed informed consent form.

Exclusion criteria

  • History of cerebrovascular disease, head trauma, hydrocephalus, brain tumors, or intracranial surgery.
  • Presence of metal implants, cardiac pacemakers, or other metallic foreign bodies (applies to PD patients).
  • Severe dyskinesia in PD patients that would compromise cooperation with video/audio recording.
  • Mini-Mental State Examination (MMSE) score ≤ 24 points.

Treatment and study plan

Video recording

Other

The patient's facial expressions and speech characteristics were recorded via video for assessment purposes.

Primary outcomes

  1. Diagnostic Accuracy of the AI Model in Assessing [ Disease/Condition ] Severity

    Time frame: Baseline

    The effectiveness of the model was demonstrated through a comparison between model-predicted scores and human expert ratings.

Study contacts

Contact information is provided by the study sponsor or research team.

Lingyan Ma, M.D.

CONTACT

[email protected]

+8613520873987

Sponsors and collaborators

Lead sponsor

Beijing Tiantan Hospital

Other

Registry information

Important dates

Study start
2023
Primary completion
2026
Study completion
2026
First posted
Feb 6, 2026
Registry last updated
Feb 6, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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