Abstract
This research was developed to investigate the effect of artificial intelligence neural network-based magnetic resonance imaging (MRI) image segmentation on the neurological function of patients with acute cerebral infarction treated with butylphthalide combined with edaravone. Eighty patients with acute cerebral infarction were selected as the research subjects, and the MRI images of patients with acute cerebral infarction were segmented by convolutional neural networks (CNN) upgraded algorithm model. MRI images of patients before and after treatment of butylphthalide combined with edaravone were compared to comprehensively evaluate the efficacy of this treatment. The results showed that compared with the traditional CNN algorithm, the running time of the CNN upgraded algorithm adopted in this study was significantly shorter, and the Loss value was lower than that of the traditional CNN model. Upgraded CNN model can realize accurate segmentation of cerebral infarction lesions in MRI images of patients. In addition, the degree of cerebral infarction and the degree of arterial stenosis were significantly improved after treatment with butylphthalide and edaravone. Compared with that before treatment, the number of patients with severe cerebral infarction or even vascular stenosis decreased significantly (P < 0.05), and gradually changed to mild vascular stenosis, and the neurological dysfunction of patients was also significantly improved. In short, MRI image segmentation based on artificial intelligence neural network can well-evaluate the efficacy and neurological impairment of butylphthalide combined with edaravone in the treatment of acute cerebral infarction, and it was worthy of promotion in clinical evaluation of the treatment effect of acute cerebral infarction.
Highlights
In this study, the magnetic resonance imaging (MRI) image segmentation based on convolutional neural networks (CNN) algorithm was used to process the MRI images of patients with acute cerebral infarction, so as to comprehensively evaluate the therapeutic effect of butylphthalide combined with edaravone in the treatment of acute cerebral infarction
CNN algorithm model was established and applied to the MRI image segmentation of patients with acute cerebral infarction to evaluate the effect of butylphthalide combined with edaravone treatment on the neurological function of patients
The results showed that MRI images based on CNN algorithm could clearly show the lesions of patients with acute cerebral infarction
Summary
Acute cerebral infarction refers to the sudden interruption of blood supply in the brain, which leads to the necrosis of brain tissue (Lyu et al, 2019). Butyphthalide significantly improves the microcirculation and blood flow in the ischemic brain area It can increase the number of capillaries in the ischemic area, reduce the infarct area of local cerebral ischemia, reduce cerebral edema, and inhibit nerve cell apoptosis. Based on the adoption of dynamic enhancement and fat suppression technology, it can accurately display the extent of the lesion, the edge, the involvement of surrounding tissues, the relationship with surrounding tissues, and the blood perfusion of the lesion, so as to accurately stage the disease It can show large occluded blood vessels, which makes up for the deficiencies of other examinations, and has guiding significance for the clinical determination of treatment plans (Drazyk et al, 2019). In this study, MRI images were segmented based on CNN algorithm to comprehensively evaluate the efficacy of butylphthalide combined with edaravone in patients with acute cerebral infarction and its influence on neurological function.
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