ArticleJournal of Parkinson's disease2023
Deep Learning Algorithm of 12-Lead Electrocardiogram for Parkinson Disease Screening.
Article in Journal of Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- The QT interval in Parkinson's disease: a systematic review.Journal of geriatric cardiology : JGC · 2024Article
- Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice.Healthcare (Basel, Switzerland) · 2024Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Standardized Database of 12-Lead Electrocardiograms with a Common Standard for the Promotion of Cardiovascular Research: KURIAS-ECG.Healthcare informatics research · 2023Article
- Epidemiology of autonomic dysfunction in Parkinson's disease (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough idiopathic Parkinson's disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagnosis of IPD. Cardiac autonomic dysfunction begins in the early stages of IPD, and an electrocardiogram (ECG) contains precise information on the heart.
objectiveThis study is to develop an ECG deep learning algorithm that can efficiently screen for IPD.
methodsData were collected from 751 IPD patients (2,138 ECGs), 751 age and sex-matched non-IPD patients (2,673 ECGs) as a control group, and 297 drug-induced Parkinsonism (DPD) patients (875 ECGs) as a disease control group. ECG data were randomly divided into training set, validation set, and test set at a ratio of 6:2:2. We developed a deep-convolutional neural network (CNN) consisting of 16 layers with Bayesian optimization that classified IPD patients by ECG data. The robustness of the deep learning model was verified through 5-fold cross-validation.
resultsThe AUROC of the model for detection of IPD was 0.924 (95% CI, 0.913-0.936) in the test set. That for detecting DPD was 0.473 (95% CI, 0.453-0.504). The sensitivities of the model according to Unified Parkinson's Disease Rating Scale III and Hoehn & Yahr scale were also similar.
conclusionIn conclusion, the CNN-based deep learning model using ECG data showed quite good performance in identifying IPD patients. Standardized 12-lead ECG test could be one of the clinically feasible candidate methods for early screening of IPD in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.