首页|Hongqi Hospital Affiliated to Mudanjiang Medical University Reports Findings in Vascular Dementia (Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment)
Hongqi Hospital Affiliated to Mudanjiang Medical University Reports Findings in Vascular Dementia (Machine learning based on the EEG and structural MRI can predict different stages of vascular cognitive impairment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Cerebrovascular Diseas es and Conditions - Vascular Dementia isthe subject of a report. According to n ews reporting originating from Mudanjiang, People’s Republicof China, by NewsRx correspondents, research stated, “Vascular cognitive impairment (VCI) is a majo rcause of cognitive impairment in the elderly and a co-factor in the developmen t and progression of most neurodegenerative diseases. With the continuing develo pment of neuroimaging, multiple markers can becombined to provide richer biolog ical information, but little is known about their diagnostic value in VCI.”Our news editors obtained a quote from the research from Hongqi Hospital Affilia ted to MudanjiangMedical University, “A total of 83 subjects participated in ou r study, including 32 patients with vascularcognitive impairment with no dement ia (VCIND), 21 patients with vascular dementia (VD), and 30normal controls (NC) . We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and co mbined them with supportvector machines to predict VCI patients at different di sease stages. The classification performance of sMRIoutperformed qEEG when dist inguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformedqEEG wh en distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed whendistinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the join t model based on qEEG andsMRI features showed relatively good classification ac curacy (AUC of 0.72) to discriminate VCIND fromNC, higher than that of either q EEG or sMRI alone. Patients at varying stages of VCI exhibit diverselevels of b rain structure and neurophysiological abnormalities. EEG serves as an affordable and convenientdiagnostic means to differentiate between different VCI stages.”
MudanjiangPeople’s Republic of ChinaAsiaBiomarkersCentral Nervous System Diseases and ConditionsCerebrovascular Diseases and ConditionsCyborgsDementiaDiagnostics and ScreeningElectro encephalographyEmerging TechnologiesHealth and MedicineMachine LearningMental HealthNeurodegenerative Diseases and ConditionsVascular Dementia
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