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Handwriting Analysis in Movement Disorders

Advanced Machine Learning Analysis of Handwriting in Patients With Movement Disorders

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05355480
Acronym
HANDWRML
Enrollment
40
Registered
2022-05-02
Start date
2022-12-01
Completion date
2023-12-31
Last updated
2023-03-23

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Movement Disorders, Parkinson Disease, Writing Disorder

Brief summary

Handwriting is a complex cognitive prowess that deteriorates in patients affected by neurodegenerative diseases, including movement disorders. More in detail, patients with Parkinson's disease (PD) may manifest prominent handwriting abnormalities which have been collectively identified as parkinsonian micrographia. MIcrographia may manifest at the onset of the disease and then worsens progressively with time. Previous techniques released to investigate micrographia in PD relied on perceptual analysis of simple tasks or were based on expensive technological tools, including tablets. However, handwriting can be promptly collected in an ecological scenario, through safe, cheap, and largely available tools. Also, the objective handwriting analysis through artificial intelligence would represent an innovative strategy even superior to previous techniques, since it allows for the analysis of large amounts of data. In this experimental project, the investigators apply a specific machine learning algorithm to analyze handwriting samples recorded in healthy controls and PD patients. The study aims to verify whether the technique proposed by the investigators would be able to detect parkinsonian micrographia objectively, monitor the evolution of handwriting abnormalities and assess the symptomatic improvement of handwriting following L-Dopa administration in PD patients.

Interventions

None listed

Sponsors

Neuromed IRCCS
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
Yes

Inclusion criteria

* Healthy conditions * clinical diagnosis of Parkinson's disease

Exclusion criteria

* cognitive decline

Design outcomes

Primary

MeasureTime frameDescription
Stroke size of handwriting charactersthrough study completion, an average of 1 yearheight of single letters

Countries

Italy

Contacts

Primary ContactAntonio Suppa, MD
antonio.suppa@uniroma1.it+00390649914074

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026