Evidence map›Paper›PMID 40622366›Full record

ArticleMovement disorders clinical practice2025

Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence.

Francesco Asci, Gaetano Saurio, Giulia Pinola, Marco Falletti, Alessandro Zampogna, Martina Patera, Francesco Fattapposta, Simone Scardapane, Antonio Suppa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Movement disorders clinical practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Francesco AsciDepartment of Neurosciences and Sensory Organs, AO San Giovanni-Addolorata, Rome, Italy.
Gaetano SaurioDepartment of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.ORCID https://orcid.org/0009-0001-2448-2281
Giulia PinolaDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
Marco FallettiDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.ORCID https://orcid.org/0000-0001-9732-1414
Alessandro ZampognaDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.ORCID https://orcid.org/0000-0002-0227-1993
Martina PateraDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
Francesco FattappostaDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
Simone ScardapaneDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy.
Antonio SuppaDepartment of Human Neurosciences, Sapienza University of Rome, Rome, Italy.ORCID https://orcid.org/0000-0001-9903-5550

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundParkinson's disease (PD) leads to handwriting abnormalities primarily characterized by micrographia. Whether micrographia manifests early in PD, worsens throughout the disease, and lastly responds to L-Dopa is still under scientific debate.

objectivesWe investigated the onset, progression and L-Dopa responsiveness of micrographia in PD, by applying a non-invasive and cheap tool of artificial intelligence- (AI)-based pen-and-paper handwriting analysis.

methodsFifty-seven PD undergoing chronic L-Dopa treatment were enrolled, including 30 early-stage (H&Y ≤ 2) and 27 mid-advanced stage (H&Y > 2) patients, alongside 25 age- and sex-matched controls. Participants completed two standardized pen-and-paper handwriting tasks in an ecological scenario. Handwriting samples were examined through clinically-based (ie, perceptual) and AI-based (ie, automatic) procedures. Both consistent (ie, average stroke size) and progressive (ie, sequential changes in stroke size) micrographia were evaluated. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the convolutional neural network (CNN) in classifying handwriting in PD and controls.

resultsClinically- and AI-based analysis revealed a general reduction in stroke size in PD supporting the concept of parkinsonian micrographia. Compared with perceptual analysis, AI-based analysis clarified that micrographia manifests early during the disease, progressively worsens and poorly responds to L-Dopa. The AI models achieved high accuracy in distinguishing PD patients from controls (91%), and moderate accuracy in differentiating early from mid-advanced PD (77%). Lastly, the AI model was not able to detect patients in OFF and ON states.

conclusionsAI-based handwriting analysis is a valuable non-invasive and cheap tool for detecting and quantifying micrographia in PD, for telemedicine purposes.

Indexed as

Artificial IntelligenceHandwritingParkinson DiseaseAgedAntiparkinson AgentsDisease ProgressionFemaleHumansLevodopaMaleMiddle AgedNeural Networks, ComputerAntiparkinson AgentsLevodopaartificial intelligencehandwritingmachine learningParkinson's diseasetelemedicine

Identifiers

PMID40622366
PMCPMC12715321

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.