Recovery from ischemic stroke at a young age

Barantsevich, Y R; Emanuel, Yu. V.

doi:10.17116/jnevro202012012249

Cited by 2 publications

(1 citation statement)

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“…Clinically, cerebral infarction accounts for nearly 80% of all strokes. Among them, cerebral thrombosis is the most common type of cerebral infarction, accounting for about 60% of all cerebral infarction [ 2 , 3 ]. At present, the pathogenesis of acute cerebral infarction has not been fully understood.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction

Liu

Zou

et al. 2022

Computational and Mathematical Methods in Medicine

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The study is aimed at exploring the application of artificial intelligence algorithm-based magnetic resonance imaging (MRI) in the diagnosis of acute cerebral infarction, expected to provide a reference for diagnosis and effect evaluation of acute cerebral infarction. In this study, 80 patients diagnosed with suspected acute cerebral infarction per Diagnostic Criteria for Cerebral Infarction were selected as the research subjects. MRI images were reconstructed by deep dictionary learning to improve their recognition ability. At the same time, the same diagnostic operation was performed by Computed Tomography (CT) images to compare with MRI. The results of the interalgorithm comparison showed the image reconstruction effect of the deep dictionary learning model is significantly better than SAE reconstruction, single-layer dictionary reconstruction model, and KAVD reconstruction. After comparison, the results of MRI based on artificial intelligence algorithm and CT evaluation were statistically significant ( P < 0.05 ). In the lesion image, the diameter of MRI lesions ( 3.81 ± 0.77 cm) based on artificial intelligence algorithm and the diameter of lesions in CT ( 3.66 ± 1.65 cm) also had significant statistical significance ( P < 0.05 ). The results showed that MRI based on deep learning was more sensitive than CT imaging for diagnosis and evaluation of patients with acute cerebral infarction, with only 1 case misdiagnosed. The rate of disease detection and lesion image quality had a higher improvement. The results can provide effective support for the clinical application of MRI based on artificial intelligence algorithm in the diagnosis of acute cerebral infarction.

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Section: Introductionmentioning

confidence: 99%