Publication:
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review

dc.contributor.authorYousefi, Milad
dc.contributor.authorAkhbari, Matin
dc.contributor.authorMohamadi, Zhina
dc.contributor.authorKarami, Shaghayegh
dc.contributor.authorDasoomi, Hediyeh
dc.contributor.authorAtabi, Alireza
dc.contributor.authorSarkeshikian, Seyed Amirali
dc.contributor.authorAbdoullahi Dehaki, Mahdi
dc.contributor.authorBayati, Hesam
dc.contributor.authorMashayekhi, Negin
dc.contributor.institutionYousefi, Milad, Shahid Beheshti University, Tehran, Iran
dc.contributor.institutionAkhbari, Matin, Faculty of Medicine, İstanbul Yeni Yüzyıl Üniversitesi, Zeytinburnu, Turkey
dc.contributor.institutionMohamadi, Zhina, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
dc.contributor.institutionKarami, Shaghayegh, School of Medicine, Tehran, Iran
dc.contributor.institutionDasoomi, Hediyeh, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
dc.contributor.institutionAtabi, Alireza, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
dc.contributor.institutionSarkeshikian, Seyed Amirali, SBUMS School of Medicine, Tehran, Iran
dc.contributor.institutionAbdoullahi Dehaki, Mahdi, Master’s of AI Engineering, Islamic Azad University, Tehran, Iran
dc.contributor.institutionBayati, Hesam, Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
dc.contributor.institutionMashayekhi, Negin, Department of Neuroscience, Bahçeşehir Üniversitesi, Istanbul, Turkey
dc.date.accessioned2025-10-05T14:52:28Z
dc.date.issued2024
dc.description.abstractBackground 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.doi10.3389/fneur.2024.1413071
dc.identifier.issn16642295
dc.identifier.scopus2-s2.0-85212784156
dc.identifier.urihttps://doi.org/10.3389/fneur.2024.1413071
dc.identifier.urihttps://hdl.handle.net/20.500.14719/7427
dc.identifier.volume15
dc.language.isoen
dc.publisherFrontiers Media SA
dc.relation.oastatusAll Open Access
dc.relation.oastatusGold Open Access
dc.relation.oastatusGreen Accepted Open Access
dc.relation.oastatusGreen Open Access
dc.relation.sourceFrontiers in Neurology
dc.subject.authorkeywordsAi
dc.subject.authorkeywordsEarly Detection
dc.subject.authorkeywordsMachine Learning
dc.subject.authorkeywordsNeurocognitive Disorder
dc.subject.authorkeywordsNeurodegenerative Disorder
dc.subject.authorkeywordsBiological Marker
dc.subject.authorkeywordsAlgorithm
dc.subject.authorkeywordsAlzheimer Disease
dc.subject.authorkeywordsAmyotrophic Lateral Sclerosis
dc.subject.authorkeywordsAnxiety
dc.subject.authorkeywordsArtificial Intelligence
dc.subject.authorkeywordsArtificial Neural Network
dc.subject.authorkeywordsBayesian Learning
dc.subject.authorkeywordsClinical Dementia Rating Scale
dc.subject.authorkeywordsCognition
dc.subject.authorkeywordsCognitive Defect
dc.subject.authorkeywordsConvolutional Neural Network
dc.subject.authorkeywordsDegenerative Disease
dc.subject.authorkeywordsDelirium
dc.subject.authorkeywordsDementia
dc.subject.authorkeywordsDemyelinating Disease
dc.subject.authorkeywordsDiscriminant Analysis
dc.subject.authorkeywordsDisease Activity
dc.subject.authorkeywordsDisorders Of Higher Cerebral Function
dc.subject.authorkeywordsEarly Cancer Diagnosis
dc.subject.authorkeywordsElectronic Health Record
dc.subject.authorkeywordsFeature Extraction
dc.subject.authorkeywordsFunctional Magnetic Resonance Imaging
dc.subject.authorkeywordsGenetic Algorithm
dc.subject.authorkeywordsGradient Boosting Classifier
dc.subject.authorkeywordsHuman
dc.subject.authorkeywordsInduced Pluripotent Stem Cell
dc.subject.authorkeywordsLearning Algorithm
dc.subject.authorkeywordsLeast Absolute Shrinkage And Selection Operator
dc.subject.authorkeywordsLogistic Regression Analysis
dc.subject.authorkeywordsMachine Learning
dc.subject.authorkeywordsMedical Practice
dc.subject.authorkeywordsMeta Analysis
dc.subject.authorkeywordsMild Cognitive Impairment
dc.subject.authorkeywordsNuclear Magnetic Resonance Imaging
dc.subject.authorkeywordsParkinson Disease
dc.subject.authorkeywordsPrincipal Component Analysis
dc.subject.authorkeywordsQuality Control
dc.subject.authorkeywordsReview
dc.subject.authorkeywordsScreening
dc.subject.authorkeywordsScreening Test
dc.subject.authorkeywordsSensitivity Analysis
dc.subject.authorkeywordsSensitivity And Specificity
dc.subject.authorkeywordsSupport Vector Machine
dc.subject.authorkeywordsSystematic Review
dc.subject.indexkeywordsbiological marker
dc.subject.indexkeywordsalgorithm
dc.subject.indexkeywordsAlzheimer disease
dc.subject.indexkeywordsamyotrophic lateral sclerosis
dc.subject.indexkeywordsanxiety
dc.subject.indexkeywordsartificial intelligence
dc.subject.indexkeywordsartificial neural network
dc.subject.indexkeywordsBayesian learning
dc.subject.indexkeywordsclinical dementia rating scale
dc.subject.indexkeywordscognition
dc.subject.indexkeywordscognitive defect
dc.subject.indexkeywordsconvolutional neural network
dc.subject.indexkeywordsdegenerative disease
dc.subject.indexkeywordsdelirium
dc.subject.indexkeywordsdementia
dc.subject.indexkeywordsdemyelinating disease
dc.subject.indexkeywordsdiscriminant analysis
dc.subject.indexkeywordsdisease activity
dc.subject.indexkeywordsdisorders of higher cerebral function
dc.subject.indexkeywordsearly cancer diagnosis
dc.subject.indexkeywordselectronic health record
dc.subject.indexkeywordsfeature extraction
dc.subject.indexkeywordsfunctional magnetic resonance imaging
dc.subject.indexkeywordsgenetic algorithm
dc.subject.indexkeywordsgradient boosting classifier
dc.subject.indexkeywordshuman
dc.subject.indexkeywordsinduced pluripotent stem cell
dc.subject.indexkeywordslearning algorithm
dc.subject.indexkeywordsleast absolute shrinkage and selection operator
dc.subject.indexkeywordslogistic regression analysis
dc.subject.indexkeywordsmachine learning
dc.subject.indexkeywordsmedical practice
dc.subject.indexkeywordsmeta analysis
dc.subject.indexkeywordsmild cognitive impairment
dc.subject.indexkeywordsnuclear magnetic resonance imaging
dc.subject.indexkeywordsParkinson disease
dc.subject.indexkeywordsprincipal component analysis
dc.subject.indexkeywordsquality control
dc.subject.indexkeywordsReview
dc.subject.indexkeywordsscreening
dc.subject.indexkeywordsscreening test
dc.subject.indexkeywordssensitivity analysis
dc.subject.indexkeywordssensitivity and specificity
dc.subject.indexkeywordssupport vector machine
dc.subject.indexkeywordssystematic review
dc.titleMachine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
dc.typeReview
dcterms.referencesHuang, 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.typePublication
local.indexed.atScopus
person.identifier.scopus-author-id58768472900
person.identifier.scopus-author-id59385391500
person.identifier.scopus-author-id59483088900
person.identifier.scopus-author-id58146650400
person.identifier.scopus-author-id59483089000
person.identifier.scopus-author-id59483089100
person.identifier.scopus-author-id59482898100
person.identifier.scopus-author-id59483089200
person.identifier.scopus-author-id59482898200
person.identifier.scopus-author-id59482898300

Files