Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI - Zhang, Lifang; Li, Yanran; Bian, Lin; Luo, Qingrong; Zhang, Xiaoxi; Zhao, Bing (Frontiers in Public Health, 2022)
This study was to explore the application of magnetic resonance imaging (MRI) based on the artificial intelligence technology combined with the neuropsychological assessment to the cognitive impairment of patients with the neurological cerebrovascular diseases. 176 patients were divided into a control group, a vascular cognitive impairment non-dementia group (VCIND), a vascular dementia group (VD), and an Alzheimer’s disease group (AD). All patients underwent MRI and neuropsychological evaluation and examination, and an improved fuzzy C-means clustering (FCM) algorithm was proposed for MRI image processing. It was found that the segmentation accuracy (SA) and similarity (KI) data of the improved FCM algorithm used in this study were higher than those of the standard FCM algorithm, BCFCM algorithm, and RFCM algorithm (P < 0.05). In the activities of Daily Living Activity (ADL), the values in the VCIND group (23.55 ± 6.12) and the VD group (28.56 ± 3.1) were higher than that in the control group (19.17 ± 3.67), so the hippocampal volume was negatively correlated with the ADL (r = -0.872, P < 0.01). In the VCIND group (52.4%), VD group (31%), and AD group (26.1%), the proportion of patients with the lacunar infarction distributed on both sides of the brain and the number of multiple cerebral infarction lesions (76.2%, 71.4%, 71.7%) were significantly higher than those in the control group (23.9% and 50%). In short, the improved FCM algorithm showed a higher segmentation effect and segmentation accuracy for MRI images of neurological cerebrovascular disease. In addition, the distribution, number, white matter lesions, and hippocampal volume of lacunar cerebral infarction were related to the cognitive impairment of patients with cerebrovascular diseases.