{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:54:12Z","timestamp":1789030452750,"version":"build-2803163510"},"reference-count":78,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cognitive impairment in Parkinson\u2019s disease (PD-CI) is a prevalent non-motor symptom, significantly diminishing quality of life and imposing a substantial family burden. Effective predictive tools are currently scarce, and the diagnostic pathway is intricate. With the growing use of artificial intelligence in healthcare, machine learning (ML) methodologies have been explored for the diagnosis and early risk prediction of PD-CI; however, their efficacy and accuracy necessitate systematic evaluation. Consequently, this investigation undertook a systematic review and meta-analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>A comprehensive literature retrieval was conducted across Web of Science, PubMed, Embase, and Cochrane Library, encompassing studies published from database inception to August 10, 2025. The PROBAST tool facilitated quality appraisal, ultimately incorporating 52 publications, of which 25 addressed diagnosis and 27 focused on risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Findings indicated that within the validation cohorts, ML models for PD-CI diagnosis achieved a c-index of 0.82, with a sensitivity of 0.57 and specificity of 0.77. For PD-CI risk prediction, the c-index reached 0.83, accompanied by a sensitivity of 0.77 and specificity of 0.76. These results suggest that ML exhibits considerable accuracy in both the diagnosis and risk prediction of PD-CI. The models primarily incorporated variables such as clinical data, genetic characteristics, biomarkers, neuroimaging, and radiomics, and no overt signs of overfitting were detected.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This research provides an evidence-based foundation for the future development of PD-CI risk prediction and intelligent diagnostic tools, thereby promoting the advancement and application of ML within Parkinson\u2019s disease and related domains.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic review registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/www.crd.york.ac.uk\/PROSPERO\/<\/jats:ext-link>\n                      , ID: CRD42023453586.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2025.1704039","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T06:38:02Z","timestamp":1765435082000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning methods for the detection and prediction of cognitive impairment in Parkinson\u2019s disease: a systematic review and meta-analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Hong","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinling","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao\u2019e","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1001\/archneur.60.3.387","article-title":"Prevalence and characteristics of dementia in Parkinson disease: an 8-year prospective study","volume":"60","author":"Aarsland","year":"2003","journal-title":"Arch. Neurol."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"45347","DOI":"10.1038\/srep45347","article-title":"Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning","volume":"7","author":"Ab\u00f3s","year":"2017","journal-title":"Sci. Rep."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1921","DOI":"10.1002\/hbm.22302","article-title":"Mild cognitive impairment in Parkinson's disease is associated with a distributed pattern of brain white matter damage","volume":"35","author":"Agosta","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1010147","DOI":"10.3389\/fneur.2022.1010147","article-title":"Machine learning can predict mild cognitive impairment in Parkinson's disease","volume":"13","author":"Amboni","year":"2022","journal-title":"Front. Neurol."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"3682","DOI":"10.1016\/j.matpr.2021.07.361","article-title":"Multiple disease prediction using machine learning algorithms","volume":"80","author":"Arumugam","year":"2023","journal-title":"Mater Today Proc"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"171","DOI":"10.14802\/jmd.23271","article-title":"Comparing Montreal cognitive assessment performance in Parkinson's disease patients: age- and education-adjusted cutoffs vs. machine learning","volume":"17","author":"Baek","year":"2024","journal-title":"J. Mov. Disord."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1375395","DOI":"10.3389\/fnins.2024.1375395","article-title":"Predicting the occurrence of mild cognitive impairment in Parkinson's disease using structural MRI data","volume":"18","author":"Beheshti","year":"2024","journal-title":"Front. Neurosci."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"619","DOI":"10.14336\/AD.2024.0323","article-title":"Euroanatomical signature of the transition from normal cognition to MCI in Parkinson's disease","volume":"16","author":"Beheshti","year":"2024","journal-title":"Aging Dis."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"1600","DOI":"10.1002\/mds.26431","article-title":"MDS research criteria for prodromal Parkinson's disease","volume":"30","author":"Berg","year":"2015","journal-title":"Mov. Disord."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3390\/a16020088","article-title":"Effective heart disease prediction using machine learning techniques","volume":"16","author":"Bhatt","year":"2023","journal-title":"Algorithms"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1199826","DOI":"10.3389\/fnagi.2023.1199826","article-title":"The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis","volume":"15","author":"Bian","year":"2023","journal-title":"Front. Aging Neurosci."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"117177","DOI":"10.1016\/j.jns.2020.117177","article-title":"Cognitive impairment in Parkinson's disease: a clinical and pathophysiological overview","volume":"419","author":"Brand\u00e3o","year":"2020","journal-title":"J. Neurol. Sci."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"105316","DOI":"10.1016\/j.parkreldis.2023.105316","article-title":"Classification and staging of Parkinson's disease using video-based eye tracking","volume":"110","author":"Brien","year":"2023","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"701499","DOI":"10.3389\/fnagi.2021.701499","article-title":"Identifying mild cognitive impairment in Parkinson's disease with electroencephalogram functional connectivity","volume":"13","author":"Cai","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"832768","DOI":"10.3389\/fnagi.2022.832768","article-title":"Retinal nerve Fiber layer thickness and associations with cognitive impairment in Parkinson's disease","volume":"14","author":"Chang","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12081048","article-title":"Prediction of cognitive degeneration in Parkinson's disease patients using a machine learning method","volume":"12","author":"Chen","year":"2022","journal-title":"Brain Sci."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"703158","DOI":"10.3389\/fnagi.2021.703158","article-title":"Development and validation of a prognostic model for cognitive impairment in Parkinson's disease with REM sleep behavior disorder","volume":"13","author":"Chen","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1186\/s12967-023-04158-8","article-title":"Detection of mild cognitive impairment in Parkinson's disease using gradient boosting decision tree models based on multilevel DTI indices","volume":"21","author":"Chen","year":"2023","journal-title":"J. Transl. Med."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"e21895","DOI":"10.1096\/fj.202100787R","article-title":"Plasma extracellular vesicles tau and \u03b2-amyloid as biomarkers of cognitive dysfunction of Parkinson's disease","volume":"35","author":"Chung","year":"2021","journal-title":"FASEB J."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"e70047","DOI":"10.1002\/hbm.70047","article-title":"Cholinergic denervation patterns in Parkinson's disease associated with cognitive impairment across domains","volume":"46","author":"d'Angremont","year":"2025","journal-title":"Hum. Brain Mapp."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2768","DOI":"10.1177\/0962280218785504","article-title":"A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes","volume":"28","author":"Debray","year":"2019","journal-title":"Stat. Methods Med. Res."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1002\/mds.26662","article-title":"Biomarkers for dementia and mild cognitive impairment in Parkinson's disease","volume":"31","author":"Delgado-Alvarado","year":"2016","journal-title":"Mov. Disord."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1002\/mds.29678","article-title":"Brain dynamics complexity as a signature of cognitive decline in Parkinson's disease","volume":"39","author":"Fiorenzato","year":"2024","journal-title":"Mov. Disord."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1002\/mds.28751","article-title":"Cognitive determinants of dysarthria in Parkinson's disease: an automated machine learning approach","volume":"36","author":"Garc\u00eda","year":"2021","journal-title":"Mov. Disord."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1016\/S1474-4422(18)30499-X","article-title":"Global, regional, and national burden of neurological disorders, 1990-2016: a systematic analysis for the global burden of disease study 2016","volume":"18","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.cger.2020.01.001","article-title":"Cognitive impairment and dementia in Parkinson disease","volume":"36","author":"Goldman","year":"2020","journal-title":"Clin. Geriatr. Med."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"e0304355","DOI":"10.1371\/journal.pone.0304355","article-title":"Machine learning for predicting cognitive decline within five years in Parkinson's disease: comparing cognitive assessment scales with DAT SPECT and clinical biomarkers","volume":"19","author":"Gorji","year":"2024","journal-title":"PLoS One"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"5093","DOI":"10.1038\/s41598-023-32345-6","article-title":"Prediction of cognitive decline in Parkinson's disease (PD) patients with electroencephalography (EEG) connectivity characterized by time-between-phase-crossing (TBPC)","volume":"13","author":"Gschwandtner","year":"2023","journal-title":"Sci. Rep."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1038\/s41531-022-00409-5","article-title":"Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease","volume":"8","author":"Harvey","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.parkreldis.2018.06.031","article-title":"Predicting early cognitive decline in newly-diagnosed Parkinson's patients: a practical model","volume":"56","author":"Hogue","year":"2018","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13101691","article-title":"Prediction of cognitive decline in Parkinson's disease using clinical and DAT SPECT imaging features, and hybrid machine learning systems","volume":"13","author":"Hosseinzadeh","year":"2023","journal-title":"Diagnostics"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"3581","DOI":"10.21037\/qims-23-1778","article-title":"A nomogram based on neuron-specific enolase and substantia nigra hyperechogenicity for identifying cognitive impairment in Parkinson's disease","volume":"14","author":"Hou","year":"2024","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"103548","DOI":"10.1016\/j.nicl.2023.103548","article-title":"Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease","volume":"41","author":"Huang","year":"2024","journal-title":"Neuroimage Clin."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1002\/mds.20974","article-title":"Subtypes of mild cognitive impairment in Parkinson's disease: progression to dementia","volume":"21","author":"Janvin","year":"2006","journal-title":"Mov. Disord."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"e14789","DOI":"10.1111\/cns.14789","article-title":"Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: a multicenter study","volume":"30","author":"Jian","year":"2024","journal-title":"CNS Neurosci. Ther."},{"key":"ref36","doi-asserted-by":"publisher","first-page":"6992","DOI":"10.1007\/s00330-022-08790-8","article-title":"Combining quantitative susceptibility mapping to radiomics in diagnosing Parkinson's disease and assessing cognitive impairment","volume":"32","author":"Kang","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"103779","DOI":"10.1016\/j.nicl.2025.103779","article-title":"Sequential patterning of dynamic brain states distinguish Parkinson's disease patients with mild cognitive impairments","volume":"46","author":"Kemp","year":"2025","journal-title":"Neuroimage Clin."},{"key":"ref38","first-page":"4845","article-title":"Automated machine learning for EEG-based classification of Parkinson's disease patients","author":"Koch","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.1002\/mds.26693","article-title":"Machine learning for large-scale wearable sensor data in Parkinson's disease: concepts, promises, pitfalls, and futures","volume":"31","author":"Kubota","year":"2016","journal-title":"Mov. Disord."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"103836","DOI":"10.1016\/j.nicl.2025.103836","article-title":"A multi-modal study on cerebrovascular dysfunction in cognitive decline of de novo Parkinson's disease","volume":"48","author":"Li","year":"2025","journal-title":"Neuroimage Clin."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"e70423","DOI":"10.1002\/brb3.70423","article-title":"Early detection and Management of Cognitive Impairment in Parkinson's disease: a predictive model approach","volume":"15","author":"Li","year":"2025","journal-title":"Brain Behav."},{"key":"ref42","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.1109\/TNSRE.2023.3324343","article-title":"Frequency-dependent microstate characteristics for mild cognitive impairment in Parkinson's disease","volume":"31","author":"Liu","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1016\/S1474-4422(17)30122-9","article-title":"Prediction of cognition in Parkinson's disease with a clinical-genetic score: a longitudinal analysis of nine cohorts","volume":"16","author":"Liu","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"107306","DOI":"10.1016\/j.parkreldis.2025.107306","article-title":"Acoustic prosodic parameters associated with Parkinson's disease cognitive impairment","volume":"132","author":"Liu","year":"2025","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s40120-025-00716-y","article-title":"A multi-omics framework based on machine learning as a predictor of cognitive impairment progression in early Parkinson's disease","volume":"14","author":"Luo","year":"2025","journal-title":"Neurol Ther."},{"key":"ref46","doi-asserted-by":"publisher","first-page":"1124232","DOI":"10.3389\/fnagi.2023.1124232","article-title":"Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and tree SHAP interpretation","volume":"15","author":"McFall","year":"2023","journal-title":"Front. Aging Neurosci."},{"key":"ref47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2046-4053-4-1","article-title":"Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement","volume":"4","author":"Moher","year":"2015","journal-title":"Syst. Rev."},{"key":"ref48","doi-asserted-by":"publisher","first-page":"105068","DOI":"10.1016\/j.neubiorev.2023.105068","article-title":"Cognition and freezing of gait in Parkinson's disease: a systematic review and meta-analysis","volume":"147","author":"Monaghan","year":"2023","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref49","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1002\/mdc3.13638","article-title":"Turning and sitting in early parkinsonism: differences between idiopathic Normal pressure hydrocephalus associated with parkinsonism and Parkinson's disease","volume":"10","author":"Mostile","year":"2023","journal-title":"Mov. Disord. Clin. Pract."},{"key":"ref50","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1016\/j.csbj.2025.02.019","article-title":"Testing machine learning algorithms to evaluate fluctuating and cognitive profiles in Parkinson's disease by motion sensors and EEG data","volume":"27","author":"Mostile","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1159\/000481568","article-title":"Behavioural and psychological symptoms of dementia: correlates and impact on caregiver distress","volume":"7","author":"Mukherjee","year":"2017","journal-title":"Dement. Geriatr. Cogn. Disord. Extra"},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.14336\/AD.2020.1225","article-title":"An update on medical and surgical treatments of Parkinson's disease","volume":"12","author":"Nemade","year":"2021","journal-title":"Aging Dis."},{"key":"ref53","doi-asserted-by":"publisher","first-page":"804991","DOI":"10.3389\/fnagi.2021.804991","article-title":"Quantitative electroencephalography as a biomarker for cognitive dysfunction in Parkinson's disease","volume":"13","author":"Novak","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref54","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3389\/fnagi.2020.00043","article-title":"\"smelling and tasting\" Parkinson's disease: using senses to improve the knowledge of the disease","volume":"12","author":"Oppo","year":"2020","journal-title":"Front. Aging Neurosci."},{"key":"ref55","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/s41531-022-00304-z","article-title":"Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test","volume":"8","author":"Ortelli","year":"2022","journal-title":"NPJ Parkinsons Dis."},{"key":"ref56","doi-asserted-by":"publisher","first-page":"e0286506","DOI":"10.1371\/journal.pone.0286506","article-title":"Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease","volume":"18","author":"Parajuli","year":"","journal-title":"PLoS One"},{"key":"ref57","first-page":"1","article-title":"Screening of mild cognitive impairment in patients with Parkinson's disease using a Variational mode decomposition based deep-learning","author":"Parajuli","year":"2023"},{"key":"ref58","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s41531-023-00566-1","article-title":"An interpretable multiparametric radiomics model of basal ganglia to predict dementia conversion in Parkinson's disease","volume":"9","author":"Park","year":"2023","journal-title":"NPJ Parkinsons Dis."},{"key":"ref59","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1002\/mds.25945","article-title":"The prevalence of Parkinson's disease: a systematic review and meta-analysis","volume":"29","author":"Pringsheim","year":"2014","journal-title":"Mov. Disord."},{"key":"ref60","doi-asserted-by":"publisher","first-page":"100189","DOI":"10.1016\/j.neuri.2025.100189","article-title":"Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease","volume":"5","author":"Putha","year":"2025","journal-title":"Neurosci. Inform."},{"key":"ref61","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.3390\/s23041985","article-title":"Identification of a gait pattern for detecting mild cognitive impairment in Parkinson's disease","volume":"23","author":"Russo","year":"2023","journal-title":"Sensors"},{"key":"ref63","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/S1474-4422(16)30328-3","article-title":"Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study","volume":"16","author":"Schrag","year":"2017","journal-title":"Lancet Neurol."},{"key":"ref64","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1002\/ana.26410","article-title":"Plasma MIA, CRP, and albumin predict cognitive decline in Parkinson's disease","volume":"92","author":"Shen","year":"2022","journal-title":"Ann. Neurol."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.18632\/aging.202368","article-title":"Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis","volume":"13","author":"Sheng","year":"2021","journal-title":"Aging (Albany NY)"},{"key":"ref66","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.parkreldis.2021.12.004","article-title":"Machine learning trained with quantitative susceptibility mapping to detect mild cognitive impairment in Parkinson's disease","volume":"94","author":"Shibata","year":"2022","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref67","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1007\/s00259-025-07070-z","article-title":"Imaging biomarkers of cortical neurodegeneration underlying cognitive impairment in Parkinson's disease","volume":"52","author":"Silva-Rodr\u00edguez","year":"2025","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/s10339-024-01197-x","article-title":"Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics","volume":"25","author":"Sivaranjini","year":"2024","journal-title":"Cogn. Process."},{"key":"ref69","doi-asserted-by":"publisher","first-page":"134","DOI":"10.12015\/issn.1674-8034.2023.07.024","article-title":"Research progress of MRI in cognitive impairment of Parkinson's disease","volume":"14","author":"Sun","year":"2023","journal-title":"Chin. J. Magn. Reson. Imaging."},{"key":"ref70","doi-asserted-by":"publisher","first-page":"136149","DOI":"10.1016\/j.neulet.2021.136149","article-title":"An individualized prediction of time to cognitive impairment in Parkinson's disease: a combined multi-predictor study","volume":"762","author":"Tang","year":"2021","journal-title":"Neurosci. Lett."},{"key":"ref71","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1007\/s10072-020-04310-y","article-title":"Eye tracking metrics to screen and assess cognitive impairment in patients with neurological disorders","volume":"41","author":"Tao","year":"2020","journal-title":"Neurol. Sci."},{"key":"ref72","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1111\/1467-9566.13622","article-title":"Constructing the detecting stage: social processes and the diagnostic journey of early onset Parkinson's disease","volume":"45","author":"Valasaki","year":"2023","journal-title":"Sociol. Health Illn."},{"key":"ref73","doi-asserted-by":"publisher","first-page":"773","DOI":"10.21037\/atm.2019.11.26","article-title":"Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls","volume":"7","author":"Wu","year":"2019","journal-title":"Ann Transl. Med."},{"key":"ref74","doi-asserted-by":"publisher","first-page":"7386","DOI":"10.1007\/s00330-020-07575-1","article-title":"Identifying Parkinson's disease with mild cognitive impairment by using combined MR imaging and electroencephalogram","volume":"31","author":"Zhang","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref75","doi-asserted-by":"publisher","first-page":"117077","DOI":"10.1016\/j.jns.2020.117077","article-title":"An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease","volume":"418","author":"Zhang","year":"2020","journal-title":"J. Neurol. Sci."},{"key":"ref76","doi-asserted-by":"publisher","first-page":"785","DOI":"10.2147\/IJGM.S399307","article-title":"Application of periventricular white matter Hyperintensities combined with homocysteine into predicting mild cognitive impairment in Parkinson's disease","volume":"16","author":"Zhang","year":"2023","journal-title":"Int. J. Gen. Med."},{"key":"ref77","doi-asserted-by":"publisher","first-page":"928980","DOI":"10.3389\/fnins.2022.928980","article-title":"Ophthalmologic problems correlates with cognitive impairment in patients with Parkinson's disease","volume":"16","author":"Zhang","year":"2022","journal-title":"Front. Neurosci."},{"key":"ref78","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1038\/s41531-024-00828-6","article-title":"Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique","volume":"10","author":"Zhu","year":"2024","journal-title":"NPJ Parkinsons Dis."}],"container-title":["Frontiers in Aging Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2025.1704039\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T06:38:06Z","timestamp":1765435086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnagi.2025.1704039\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,11]]},"references-count":78,"alternative-id":["10.3389\/fnagi.2025.1704039"],"URL":"https:\/\/doi.org\/10.3389\/fnagi.2025.1704039","relation":{},"ISSN":["1663-4365"],"issn-type":[{"value":"1663-4365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,11]]},"article-number":"1704039"}}