Abstract
This work aimed to investigate the application of positron emission tomography (PET) molecular imaging based on the deep learning algorithm in the assessment of cognitive dysfunction in patients with epilepsy. In this study, 52 hospitalized patients with epilepsy were selected as the epilepsy group and treated with different kinds of antiepileptic drugs, and 52 volunteers were selected as the control group. A U-net optimized network structure algorithm based on deep learning was proposed in this study and compared with a fully convolutional neural network (FCNN). Besides, it was applied in the PET molecular imaging of patients with epilepsy, and the segmentation effect of the U-net optimized network structure was good. According to event-related potential examinations, the proportion of patients with cognitive dysfunction in the epilepsy group (74.19%) was higher than the proportion of the control group (7.46%) ( P < 0.05 ). The patients with cognitive dysfunction (57.89%) who took one antiepileptic drug were lower than those with two antiepileptic drugs (84.61%) ( P < 0.05 ). The difference was statistically obvious in the overall quality of life of patients with epilepsy ( P < 0.05 ). The occurrence of cognitive dysfunction in patients with epilepsy was related to the type of seizures. In addition, the quality of life of patients who suffered from cognitive dysfunction was low.
Highlights
Epilepsy is a group of chronic diseases and syndromes of central nervous system dysfunction caused by abnormal excessive discharge of brain neurons [1]
E U-net optimized network structure algorithm was proposed based on deep learning, compared with the fully convolutional neural network (FCNN) structure, and applied to the positron emission tomography (PET) molecular images of 52 patients with epilepsy. e objective of this study was to explore the PET molecular imaging to assess the influencing factors of cognitive dysfunction in patients with epilepsy and the quality of life of patients with epilepsy
It was applied in the PET molecular images of 52 patients with epilepsy. e U-net optimized network structure contained 4 densely connected blocks in the upsampling and downsampling paths, and there was a densely connected block between the two. e number of densely connected blocks was different from the incremental rate
Summary
Epilepsy is a group of chronic diseases and syndromes of central nervous system dysfunction caused by abnormal excessive discharge of brain neurons [1]. Epilepsy occurs in people of any age, region, and ethnicity. The incidence of cerebrovascular disease, dementia, and neurodegenerative diseases has increased with the aging of the Chinese population. The incidence of epilepsy in the elderly shows an upward trend [2]. According to the location of abnormal discharge of neurons and the range of discharge, the main clinical manifestations are the different dysfunctions, including different motor, sensory, cognitive, and autonomic nerve [3]. According to epidemiological research data, the annual prevalence of epilepsy is about
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