ReviewSensors (Basel, Switzerland)2021
Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Surface Electromyography for Parkinson's Disease Monitoring: A Review of Machine and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2026Review
- Pathology-Anchored Transcranial Sonography: A Cascaded Super-Resolution Deep Learning System for Early-Stage Parkinson's Disease Grading.NPJ Parkinson's disease · 2026Article
- Assessing Time-Frequency Analysis Methods for Non-Stationary EMG Bursts: Application to an Animal Model of Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Structural and Statistical Knowledge-Enhanced Attention Network for early Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Secure and interpretable lung cancer prediction model using mapreduce private blockchain federated learning and XAI.Scientific reports · 2025Article
- Deep Learning to Differentiate Parkinsonian Syndromes Using Multimodal Magnetic Resonance Imaging: A Proof-of-Concept Study.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- A Deep Learning and Explainable Artificial Intelligence based Scheme for Breast Cancer Detection.Scientific reports · 2025Article
- Sway frequencies may predict postural instability in Parkinson's disease: a novel convolutional neural network approach.Journal of neuroengineering and rehabilitation · 2025Article
- Combined with multimodal medical imaging and artificial intelligence for early diagnosis of Parkinson's disease.Frontiers in neurology · 2025Article
- Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches.Computational and structural biotechnology journal · 2024Review
- Late feature fusion using neural network with voting classifier for Parkinson's disease detection.BMC medical informatics and decision making · 2024Article
- Enhancing early Parkinson's disease detection through multimodal deep learning and explainable AI: insights from the PPMI database.Scientific reports · 2024Article
- A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.Sensors (Basel, Switzerland) · 2024Article
- Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.Frontiers in medicine · 2023Article
- The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease.Frontiers in aging neuroscience · 2023Review
- Can Gait Features Help in Differentiating Parkinson's Disease Medication States and Severity Levels? A Machine Learning Approach.Sensors (Basel, Switzerland) · 2022Article
- Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data.Biomedicines · 2022Article
- Preclinical reserpine models recapitulating motor and non-motor features of Parkinson's disease: Roles of epigenetic upregulation of alpha-synuclein and autophagy impairment.Frontiers in pharmacology · 2022Article
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
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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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.