Evidence map›Paper›PMID 41696087›Full record

ArticleDigital health

DP-MDLA Net: Detection of smooth pursuit abnormalities in Parkinson's disease.

Zhiyuan Tan, Jie Zhao, Liwen Kong, Qinfen Song, Qijie Zou, Yana Lv, Shanshan Liang, Shuai Tao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zhiyuan TanCollege of Information Engineering, Dalian University, Dalian, Liaoning, China.ORCID https://orcid.org/0009-0000-3693-9798
Jie ZhaoDepartment of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
Liwen KongCollege of Information Engineering, Dalian University, Dalian, Liaoning, China.
Qinfen SongDepartment of Anus and Intestine Surgery, Dalian University Affiliated Xinhua Hospital, Dalian, Liaoning, China.
Qijie ZouCollege of Information Engineering, Dalian University, Dalian, Liaoning, China.
Yana LvCollege of Information Engineering, Dalian University, Dalian, Liaoning, China.
Shanshan LiangThe Key Laboratory of Biomarker High-Throughput Screening and Target Translation of Breast and Gastrointestinal Tumors, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
Shuai TaoCollege of Information Engineering, Dalian University, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To develop and validate a smartphone video-based framework using deep learning for quantifying smooth-pursuit abnormalities in Parkinson's disease. Methods: Smartphone videos ( Results: Under a random split regimen for event samples, the framework achieved 96.59% accuracy, 97.50% precision, 95.12% recall, 96.03% F1-score, and an AUC of 0.9939 on the test set ( Conclusion: The framework enables robust event-level quantification of smooth-pursuit abnormalities from smartphone video, supporting portable bedside assessment and standardized longitudinal monitoring of Parkinson's disease without specialized equipment.

Indexed as

computer visiondeep learningeye movement analysisParkinson’s diseasesmartphonesmooth pursuit

Identifiers

PMID41696087
PMCPMC12901912

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Registered trials

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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.