Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI.

Lifang Zhang,Qingrong Luo,Xiaoxi Zhang,Yanran Li,Bing Zhao,Lin Bian

doi:10.3389/fpubh.2021.813641

Abstract

This study was to explore the application of MRI based on artificial intelligence technology combined with neuropsychological assessment to the cognitive impairment of patients with neurological cerebrovascular diseases. A total of 176 patients were divided into a control group, a vascular cognitive impairment non-dementia (VCIND) group, a vascular dementia (VD) group, and an Alzheimer's disease (AD) group. All patients underwent MRI and neuropsychological evaluation and examination, and an improved fuzzy C-means (FCM) clustering algorithm was proposed for MRI 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, bias-corrected FCM (BCFCM) algorithm, and rough FCM (RFCM) algorithm (p < 0.05). In the activities of daily living (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, and 71.7%, respectively) 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 SA for MRI 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.

Highlights

Cerebrovascular disease is one of the main factors to cause vascular cognitive impairment (VCI) [1, 2]
The standard fuzzy C-means (FCM) algorithm was introduced, the bias-corrected FCM (BCFCM) improved based on the global information, and compared the segmentation effect based on the rough FCM (RFCM) algorithm were compared with the algorithm proposed in this study
The detection by the MRI based on artificial intelligence technology will play an important role in the early diagnosis and treatment of diseases related to vascular cognitive impairment

Summary

Introduction

Cerebrovascular disease is one of the main factors to cause vascular cognitive impairment (VCI) [1, 2]. With the aging of current society, the incidence of cerebrovascular disease and VCI has increased significantly, ranking second only to Alzheimer’s disease (AD), and is an important cause of the cognitive decline in the elderly [3]. It is the same as other cognitive diseases, and its clinical characteristics are gait, mood, behavior, and urination disorders [4]. When the early cognitive impairment occurs, the condition will be mild so that it will often be ignored by others. When it develops into a disorder or even dementia, it is already too late and is not easy to diagnose and treat [5, 6]. It is very important to detect and find the vascular dysfunction as early as possible

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Discussion

Conclusion