ArticleSensors (Basel, Switzerland)2019
Feasible Classified Models for Parkinson Disease from
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled 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.
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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Correlation of Parkinson's disease severity andQuantitative imaging in medicine and surgery · 2025Article
- A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.BMC neurology · 2024Article
- Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Radiomics incorporating deep features for predicting Parkinson's disease inEJNMMI physics · 2024Article
- PARKINSON'S DISEASE CLASSIFICATION USING CONTRASTIVE GRAPH CROSS-VIEW LEARNING WITH MULTIMODAL FUSION OF SPECT IMAGES AND CLINICAL FEATURES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI andCanadian journal of psychiatry. Revue canadienne de psychiatrie · 2024Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.Diagnostics (Basel, Switzerland) · 2022Review
- Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features.Frontiers in aging neuroscience · 2022Article
- A Shape Approximation for Medical Imaging Data.Sensors (Basel, Switzerland) · 2020Article
- A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure.Frontiers in neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with
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.