SynthesisFrontiers in neurology2024
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.
Synthesis in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses 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.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Pooled it
- Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.Biomedical engineering online · 2025Pooled it
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- The role of brain health and resilience in reshaping trajectories of late-life neuropsychiatric disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Data-driven clinical decision support tool for diagnosing mild cognitive impairment in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- A narrative review on artificial intelligence in neurosurgery: ethical challenges and implementation considerations.Annals of medicine and surgery (2012) · 2025Review
- Bioinformatics Analysis of Persistent Dysregulated Expression of Genes Involved in HCV-Induced Neurological Disorders and Liver Injuries After DAA Treatment Through Weighted Gene Co-Expression Network Analysis.Health science reports · 2025Article
- Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology.Biomimetics (Basel, Switzerland) · 2025Review
- A Machine Learning Approach to Modify the Neurocognitive Frailty Index for the Prediction of Cognitive Status in the Canadian Population.Journal of clinical medicine · 2025Article
- The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson's Disease Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.Diagnostics (Basel, Switzerland) · 2025Review
- Applications of deep learning in intracranial aneurysm imaging: A scoping review of detection, risk prediction, and emerging prognostic models.Current journal of neurology · 2025Review
- AI-Y: An AI Checklist for Population Ethics Across the Global Context.Current epidemiology reports · 2025Review
- Identifying depression with mixed features: the potential value of eye-tracking features.Frontiers in neurology · 2025Article
- A lightweight cerebrospinal fluid biomarker-based model for first-diagnosis prediction of Parkinson's disease: model development, external validation, and local deployment.Frontiers in aging neuroscience · 2025Article
- Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aim: Neurodegenerative disorders (e.g., Alzheimer's, Parkinson's) lead to neuronal loss; neurocognitive disorders (e.g., delirium, dementia) show cognitive decline. Early detection is crucial for effective management. Machine learning aids in more precise disease identification, potentially transforming healthcare. This comprehensive systematic review discusses how machine learning (ML), can enhance early detection of these disorders, surpassing traditional diagnostics' constraints. Methods: In this review, databases were examined up to August 15th, 2023, for ML data on neurodegenerative and neurocognitive diseases using PubMed, Scopus, Google Scholar, and Web of Science. Two investigators used the RAYYAN intelligence tool for systematic reviews to conduct the screening. Six blinded reviewers reviewed titles/abstracts. Cochrane risk of bias tool was used for quality assessment. Results: Our search found 7,069 research studies, of which 1,365 items were duplicates and thus removed. Four thousand three hundred and thirty four studies were screened, and 108 articles met the criteria for inclusion after preprocessing. Twelve ML algorithms were observed for dementia, showing promise in early detection. Eighteen ML algorithms were identified for Parkinson's, each effective in detection and diagnosis. Studies emphasized that ML algorithms are necessary for Alzheimer's to be successful. Fourteen ML algorithms were discovered for mild cognitive impairment, with LASSO logistic regression being the only one with unpromising results. Conclusion: This review emphasizes the pressing necessity of integrating verified digital health resources into conventional medical practice. This integration may signify a new era in the early detection of neurodegenerative and neurocognitive illnesses, potentially changing the course of these conditions for millions globally. This study showcases specific and statistically significant findings to illustrate the progress in the area and the prospective influence of these advancements on the global management of neurocognitive and neurodegenerative illnesses.
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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.