Evidence map›Paper›PMID 39130458›Full record

ArticleHeliyon2024

PD-DETECTOR: A sustainable and computationally intelligent mobile application model for Parkinson's disease severity assessment.

Sushruta Mishra, Lambodar Jena, Nilamadhab Mishra, Hsien-Tsung Chang

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Frontiers in aging neuroscience · 2026
    Review
  2. Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025
    Review
  3. Article
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

4 authors.

Sushruta MishraSchool of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India.
Lambodar JenaCenter for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be)University, Bhubaneswar, India.
Nilamadhab MishraSchool of Computing Science and Engineering, VIT Bhopal University, Sehore, India.
Hsien-Tsung ChangBachelor Program in Artificial Intelligence, Chang Gung University, Taoyuan, 333, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper introduces a mobile cloud-based predictive model for assisting Parkinson's disease (PD) patients. PD, a chronic neurodegenerative disorder, impairs motor functions and daily tasks due to the degeneration of dopamine-producing neurons in the brain. The model utilizes smartphones to aid patients in collecting voice samples, which are then sent to a cloud service for storage and processing. A hybrid deep learning model, trained using the UCI Parkinson's Telemonitoring Voice dataset, analyzes this data to estimate the severity of PD symptoms. The model's performance is noteworthy, with accuracy, sensitivity, and specificity metrics of 96.2 %, 94.15 %, and 96.15 %, respectively. Additionally, it boasts a rapid response time of just 13 s. Results are delivered to users via smartphone alert notifications, coupled with a knowledge base feature that educates them about PD. This system provides reliable home-based assessment and monitoring of PD and enables prompt medical intervention, significantly enhancing the quality of life for patients with Parkinson's disease.

Indexed as

Deep neural network (DNN)m-HealthcareParkinson's diseaseTelemonitoringVoice dataset

Identifiers

PMID39130458
PMCPMC11315181

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.