Evidence map›Paper›PMID 38332372›Full record

ArticleNPJ digital medicine2024

FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation.

Yu-Yuan Yang, Ming-Yang Ho, Chung-Hwei Tai, Ruey-Meei Wu, Ming-Che Kuo, Yufeng Jane Tseng

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
5.9field-weighted citation impact, top 3% of its field
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

13 citing papers in PubMed, 16 citations in OpenAlex.

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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

6 authors at 3 institutions in 1 country.

Yu-Yuan YangGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.ORCID http://orcid.org/0000-0003-3689-6964
Ming-Yang HoDepartment of Computer Science and Information Engineering, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC.ORCID http://orcid.org/0000-0002-1767-7494
Chung-Hwei TaiDepartment of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC.
Ruey-Meei WuDepartment of Medicine, National Taiwan University Cancer Center, No. 57, Lane 155, Sec. 3, Keelung Rd., Da'an Dist., Taipei City, 106, Taiwan, ROC.
Ming-Che KuoDepartment of Neurology, National Taiwan University Hospital, No. 1 Changde St., Zhongzheng Dist., Taipei City, 100229, Taiwan, ROC. kuomingche0402@gmail.com.ORCID http://orcid.org/0000-0003-3688-0225
Yufeng Jane TsengGraduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, No. 1 Roosevelt Rd. Sec. 4, Taipei, 10617, Taiwan, ROC. yjtseng@csie.ntu.edu.tw.ORCID http://orcid.org/0000-0002-8461-6181
National Taiwan University · TWNational Taiwan University of Science and Technology · TWNational Taiwan University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients.

Identifiers

PMID38332372
PMCPMC10853559
OpenAlexW4391657938

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.