Exploration of the influence of early rehabilitation training on circulating endothelial progenitor cell mobilization in patients with acute ischemic stroke and its related mechanism under a lightweight artificial intelligence algorithm.

OBJECTIVE: This work aimed to explore the application of lightweight artificial intelligence algorithms in magnetic resonance imaging (MRI) image processing of patients with acute ischemic stroke (AIS) to clarify the effect and mechanism of early rehabilitation training on the mobilization of circulating endothelial progenitor cells (EPCs) in AIS. PATIENTS AND METHODS: A total of 98 AIS patients undergoing MRI examination were selected as the research objects and were randomly divided into a rehabilitation group (early rehabilitation training, 50 cases) and a routine group (conventional treatment, 48 cases) by random number table method and lottery method. In this work, based on the convolutional neural network (CNN) algorithm, a low-rank decomposition algorithm was introduced to optimize it, and a lightweight MRI image computer intelligent segmentation model (LT-RCNN) was established. The LT-RCNN model was used in the MRI image processing of AIS patients, and the role of the model in AIS image segmentation and lesion localization was analyzed. Furthermore, flow cytometry was used to detect the number of peripheral circulating EPCs and CD34+KDR+ cells in the two groups of patients before and after treatment. The serum levels of vascular endothelial growth factor (VEGF), tumor necrosis factor-α (TNF-α), interleukin 10 (IL-10), and stromal cell-derived factor-1α (SDF-1α) content were detected by Enzyme-Linked Immunosorbent Assay (ELISA). In addition, the correlation between each factor and CD34+KDR+ was analyzed by Pearson linear correlation. RESULTS: The diffusion-weighted imaging (DWI) signal of MRI images of AIS patients under the LT-RCNN model was high. The location of the lesion could be accurately detected, and the contour of the lesion could be displayed and segmented, and the segmentation accuracy and sensitivity were significantly better than before optimization. The number of EPCs and CD34+KDR+ cells in the rehabilitation group was increased compared with the control group (p<0.01); the expression levels of VEGF, IL-10, and SDF-1α were higher than those of the control group (p<0.001), and TNF-α content was lower than the control group (p<0.001). The number of CD34+KDR+ cells was positively correlated with VEGF, IL-10, and TNF-α contents (p<0.01). CONCLUSIONS: The results showed that the computer-intelligent segmentation model LT-RCNN could accurately locate, and segment AIS lesions and the early rehabilitation training could change the expression level of inflammatory factors and further promote the mobilization of AIS circulation EPCs.