Publication: Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
| dc.contributor.author | Yousefi, Milad | |
| dc.contributor.author | Akhbari, Matin | |
| dc.contributor.author | Mohamadi, Zhina | |
| dc.contributor.author | Karami, Shaghayegh | |
| dc.contributor.author | Dasoomi, Hediyeh | |
| dc.contributor.author | Atabi, Alireza | |
| dc.contributor.author | Sarkeshikian, Seyed Amirali | |
| dc.contributor.author | Abdoullahi Dehaki, Mahdi | |
| dc.contributor.author | Bayati, Hesam | |
| dc.contributor.author | Mashayekhi, Negin | |
| dc.contributor.institution | Yousefi, Milad, Shahid Beheshti University, Tehran, Iran | |
| dc.contributor.institution | Akhbari, Matin, Faculty of Medicine, İstanbul Yeni Yüzyıl Üniversitesi, Zeytinburnu, Turkey | |
| dc.contributor.institution | Mohamadi, Zhina, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran | |
| dc.contributor.institution | Karami, Shaghayegh, School of Medicine, Tehran, Iran | |
| dc.contributor.institution | Dasoomi, Hediyeh, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran | |
| dc.contributor.institution | Atabi, Alireza, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran | |
| dc.contributor.institution | Sarkeshikian, Seyed Amirali, SBUMS School of Medicine, Tehran, Iran | |
| dc.contributor.institution | Abdoullahi Dehaki, Mahdi, Master’s of AI Engineering, Islamic Azad University, Tehran, Iran | |
| dc.contributor.institution | Bayati, Hesam, Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran | |
| dc.contributor.institution | Mashayekhi, Negin, Department of Neuroscience, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.date.accessioned | 2025-10-05T14:52:28Z | |
| dc.date.issued | 2024 | |
| dc.description.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. © 2024 Elsevier B.V., All rights reserved. | |
| dc.identifier.doi | 10.3389/fneur.2024.1413071 | |
| dc.identifier.issn | 16642295 | |
| dc.identifier.scopus | 2-s2.0-85212784156 | |
| dc.identifier.uri | https://doi.org/10.3389/fneur.2024.1413071 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14719/7427 | |
| dc.identifier.volume | 15 | |
| dc.language.iso | en | |
| dc.publisher | Frontiers Media SA | |
| dc.relation.oastatus | All Open Access | |
| dc.relation.oastatus | Gold Open Access | |
| dc.relation.oastatus | Green Accepted Open Access | |
| dc.relation.oastatus | Green Open Access | |
| dc.relation.source | Frontiers in Neurology | |
| dc.subject.authorkeywords | Ai | |
| dc.subject.authorkeywords | Early Detection | |
| dc.subject.authorkeywords | Machine Learning | |
| dc.subject.authorkeywords | Neurocognitive Disorder | |
| dc.subject.authorkeywords | Neurodegenerative Disorder | |
| dc.subject.authorkeywords | Biological Marker | |
| dc.subject.authorkeywords | Algorithm | |
| dc.subject.authorkeywords | Alzheimer Disease | |
| dc.subject.authorkeywords | Amyotrophic Lateral Sclerosis | |
| dc.subject.authorkeywords | Anxiety | |
| dc.subject.authorkeywords | Artificial Intelligence | |
| dc.subject.authorkeywords | Artificial Neural Network | |
| dc.subject.authorkeywords | Bayesian Learning | |
| dc.subject.authorkeywords | Clinical Dementia Rating Scale | |
| dc.subject.authorkeywords | Cognition | |
| dc.subject.authorkeywords | Cognitive Defect | |
| dc.subject.authorkeywords | Convolutional Neural Network | |
| dc.subject.authorkeywords | Degenerative Disease | |
| dc.subject.authorkeywords | Delirium | |
| dc.subject.authorkeywords | Dementia | |
| dc.subject.authorkeywords | Demyelinating Disease | |
| dc.subject.authorkeywords | Discriminant Analysis | |
| dc.subject.authorkeywords | Disease Activity | |
| dc.subject.authorkeywords | Disorders Of Higher Cerebral Function | |
| dc.subject.authorkeywords | Early Cancer Diagnosis | |
| dc.subject.authorkeywords | Electronic Health Record | |
| dc.subject.authorkeywords | Feature Extraction | |
| dc.subject.authorkeywords | Functional Magnetic Resonance Imaging | |
| dc.subject.authorkeywords | Genetic Algorithm | |
| dc.subject.authorkeywords | Gradient Boosting Classifier | |
| dc.subject.authorkeywords | Human | |
| dc.subject.authorkeywords | Induced Pluripotent Stem Cell | |
| dc.subject.authorkeywords | Learning Algorithm | |
| dc.subject.authorkeywords | Least Absolute Shrinkage And Selection Operator | |
| dc.subject.authorkeywords | Logistic Regression Analysis | |
| dc.subject.authorkeywords | Machine Learning | |
| dc.subject.authorkeywords | Medical Practice | |
| dc.subject.authorkeywords | Meta Analysis | |
| dc.subject.authorkeywords | Mild Cognitive Impairment | |
| dc.subject.authorkeywords | Nuclear Magnetic Resonance Imaging | |
| dc.subject.authorkeywords | Parkinson Disease | |
| dc.subject.authorkeywords | Principal Component Analysis | |
| dc.subject.authorkeywords | Quality Control | |
| dc.subject.authorkeywords | Review | |
| dc.subject.authorkeywords | Screening | |
| dc.subject.authorkeywords | Screening Test | |
| dc.subject.authorkeywords | Sensitivity Analysis | |
| dc.subject.authorkeywords | Sensitivity And Specificity | |
| dc.subject.authorkeywords | Support Vector Machine | |
| dc.subject.authorkeywords | Systematic Review | |
| dc.subject.indexkeywords | biological marker | |
| dc.subject.indexkeywords | algorithm | |
| dc.subject.indexkeywords | Alzheimer disease | |
| dc.subject.indexkeywords | amyotrophic lateral sclerosis | |
| dc.subject.indexkeywords | anxiety | |
| dc.subject.indexkeywords | artificial intelligence | |
| dc.subject.indexkeywords | artificial neural network | |
| dc.subject.indexkeywords | Bayesian learning | |
| dc.subject.indexkeywords | clinical dementia rating scale | |
| dc.subject.indexkeywords | cognition | |
| dc.subject.indexkeywords | cognitive defect | |
| dc.subject.indexkeywords | convolutional neural network | |
| dc.subject.indexkeywords | degenerative disease | |
| dc.subject.indexkeywords | delirium | |
| dc.subject.indexkeywords | dementia | |
| dc.subject.indexkeywords | demyelinating disease | |
| dc.subject.indexkeywords | discriminant analysis | |
| dc.subject.indexkeywords | disease activity | |
| dc.subject.indexkeywords | disorders of higher cerebral function | |
| dc.subject.indexkeywords | early cancer diagnosis | |
| dc.subject.indexkeywords | electronic health record | |
| dc.subject.indexkeywords | feature extraction | |
| dc.subject.indexkeywords | functional magnetic resonance imaging | |
| dc.subject.indexkeywords | genetic algorithm | |
| dc.subject.indexkeywords | gradient boosting classifier | |
| dc.subject.indexkeywords | human | |
| dc.subject.indexkeywords | induced pluripotent stem cell | |
| dc.subject.indexkeywords | learning algorithm | |
| dc.subject.indexkeywords | least absolute shrinkage and selection operator | |
| dc.subject.indexkeywords | logistic regression analysis | |
| dc.subject.indexkeywords | machine learning | |
| dc.subject.indexkeywords | medical practice | |
| dc.subject.indexkeywords | meta analysis | |
| dc.subject.indexkeywords | mild cognitive impairment | |
| dc.subject.indexkeywords | nuclear magnetic resonance imaging | |
| dc.subject.indexkeywords | Parkinson disease | |
| dc.subject.indexkeywords | principal component analysis | |
| dc.subject.indexkeywords | quality control | |
| dc.subject.indexkeywords | Review | |
| dc.subject.indexkeywords | screening | |
| dc.subject.indexkeywords | screening test | |
| dc.subject.indexkeywords | sensitivity analysis | |
| dc.subject.indexkeywords | sensitivity and specificity | |
| dc.subject.indexkeywords | support vector machine | |
| dc.subject.indexkeywords | systematic review | |
| dc.title | Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review | |
| dc.type | Review | |
| dcterms.references | Huang, Gao, Trends in extreme learning machines: A review, Neural Networks, 61, pp. 32-48, (2015), Ahsan, Md Manjurul, Machine learning-based heart disease diagnosis: A systematic literature review, Artificial Intelligence in Medicine, 128, (2022), Ma, Fuzhe, Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network, Future Generation Computer Systems, 111, pp. 17-26, (2020), Apostolopoulos, Ioannis D., Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Physical and Engineering Sciences in Medicine, 43, 2, pp. 635-640, (2020), Yahyaoui, Amani, A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques, (2019), Méndez, Mario F., Neurodegenerative Disorders, pp. 840-844, (2007), Sachdev, Perminder S., Classifying neurocognitive disorders: The DSM-5 approach, Nature Reviews Neurology, 10, 11, pp. 634-642, (2014), Rizzo, Giovanni, Accuracy of clinical diagnosis of Parkinson disease, Neurology, 86, 6, pp. 566-576, (2016), Zou, Yizhuang, Detection and diagnosis of delirium in the elderly: Psychiatrist diagnosis, confusion assessment method, or consensus diagnosis?, International Psychogeriatrics, 10, 3, pp. 303-308, (1998), Page, Matthew James, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews, International Journal of Surgery, 88, (2021) | |
| dspace.entity.type | Publication | |
| local.indexed.at | Scopus | |
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