scholarly journals Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Rui Yang ◽  
Ying Zhang ◽  
Miao Xu ◽  
Jing Ma
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Acute Stroke ◽  
Image Features ◽  
Neurological Recovery ◽  
Control Group ◽  
Rehabilitation Care ◽  
Comprehensive Rehabilitation ◽  
Experimental Group

This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 patients with acute stroke who were treated in hospital were selected as the research objects, and they were rolled into a control group (routine care) and an experimental group (comprehensive rehabilitation care). The dense dilated block-convolution neural network (DD-CNN) algorithm under deep learning for cerebrovascular was adopted to assess the effect of comprehensive rehabilitation care on the neurological recovery of patients with acute stroke. The results showed that the Berg scale scores, Fugl-Meyer scores, and Functional Independence Measure (FIM) scores of the experimental group of patients after 6 weeks and 12 weeks of comprehensive rehabilitation nursing were greatly different from those before treatment, showing statistical differences ( P < 0.05 ). Compared with conventional magnetic resonance imaging (MRI) images, MRA images based on CNN algorithm, Dense Net algorithm, and DD-CNN algorithm can more clearly show the patient’s cerebral artery occlusion. The average dice similarity coefficient (DSC) values of CNN algorithm, Dense Net algorithm, and DD-CNN algorithm were determined to be 84.3%, 95.7%, and 97.8%, respectively; the average sensitivity (Sen) values of the three algorithms were 76.1%, 95.4%, and 96.8%, respectively; and the average accuracy (Acc) values were 87.9%, 96.3%, and 97.9%, respectively. Thus, there were statistically obvious differences among the three algorithms in terms of average values of DSC, Sen, and Acc ( P < 0.05 ). The MRA images processed by the DD-CNN algorithm showed that the degree of neurological recovery of the experimental group was observably greater than that of the control group, and the difference was statistically obvious ( P < 0.05 ). In short, the image features of MRA based on the deep learning DD-CNN algorithm showed good application value in studying the effect of comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke, and it was worthy of promotion.

scholarly journals Magnetic Resonance Imaging Features under Deep Learning Algorithms in Evaluated Cerebral Protection of Craniotomy Evacuation of Hematoma under Propofol Anesthesia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Deqian Xin ◽  
Zhongzhe An ◽  
Juan Ding ◽  
Zhi Li ◽  
Leyan Qiao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Super Resolution ◽  
Control Group ◽  
Imaging Features ◽  
Resonance Imaging ◽  
Resolution Algorithm ◽  
Experimental Group

This study aimed to explore the value of magnetic resonance imaging (MRI) features based on deep learning super-resolution algorithms in evaluating the value of propofol anesthesia for brain protection of patients undergoing craniotomy evacuation of the hematoma. An optimized super-resolution algorithm was obtained through the multiscale network reconstruction model based on the traditional algorithm. A total of 100 patients undergoing craniotomy evacuation of hematoma were recruited and rolled into sevoflurane control group and propofol experimental group. Both were evaluated using diffusion tensor imaging (DTI) images based on deep learning super-resolution algorithms. The results showed that the fractional anisotropic image (FA) value of the hind limb corticospinal tract of the affected side of the internal capsule of the experimental group after the operation was 0.67 ± 0.28. The National Institute of Health Stroke Scale (NIHSS) score was 6.14 ± 3.29. The oxygen saturation in jugular venous (SjvO2) at T4 and T5 was 61.93 ± 6.58% and 59.38 ± 6.2%, respectively, and cerebral oxygen uptake rate (CO2ER) was 31.12 ± 6.07% and 35.83 ± 7.91%, respectively. The difference in jugular venous oxygen (Da-jvO2) at T3, T4, and T5 was 63.28 ± 10.15 mL/dL, 64.89 ± 13.11 mL/dL, and 66.03 ± 11.78 mL/dL, respectively. The neuron-specific enolase (NSE) and central-nerve-specific protein (S100β) levels at T5 were 53.85 ± 12.31 ng/mL and 7.49 ± 3.16 ng/mL, respectively. In terms of the number of postoperative complications, the patients in the experimental group were better than the control group under sevoflurane anesthesia, and the differences were substantial ( P  < 0.05). In conclusion, MRI images based on deep learning super-resolution algorithm have great clinical value in evaluating the degree of brain injury in patients anesthetized with propofol and the protective effect of propofol on brain nerves.

scholarly journals Deep Learning-Based Computed Tomography Image Features in the Detection and Diagnosis of Perianal Abscess Tissue

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Song Han ◽  
Jun Yang ◽  
Jihua Xu
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Perianal Abscess ◽  
Ct Images ◽  
Image Features ◽  
Control Group ◽  
Dice Coefficient ◽  
Multislice Spiral Ct ◽  
Detection And Diagnosis ◽  
Experimental Group

The performance characteristics of deep learning fully convolutional neural network (DLFCNN) algorithm-based computed tomography (CT) images were investigated in the detection and diagnosis of perianal abscess tissue. 60 patients who were medically diagnosed as perianal abscesses in the hospital were selected as the experimental group, and 60 healthy volunteers were selected as the control group. In this study, the DLFCNN algorithm based on deep learning was compared with the CNN algorithm and applied to the segmentation training of CT images of patients with perianal abscesses. Then, the segmentation metrics Jaccard, Dice coefficient, precision rate, and recall rate were compared by extracting the region of interest. The results showed that Jaccard (0.7326) calculated by the CNN algorithm was sharply lower than that of the DLFCNN algorithm (0.8525), and the Dice coefficient (0.7264) was also steeply lower than that of the DLFCNN algorithm (0.8434) ( P < 0.05 ). The thickness range of the epidermis and dermis in patients from the experimental group was 4.1–4.9 mm, which was markedly greater than the range of the control group (1.8–3.6 mm) ( P < 0.05 ). Besides, the CT value of the subcutaneous fascia in the experimental group (−95.45 ± 8.26) hugely reduced compared with the control group (−76.34 ± 7.69) ( P < 0.05 ). The accuracy rate of the patients with perianal abscesses was 96.67% by multislice spiral CT (MSCT). Therefore, the DLFCNN algorithm in this study had good stability and good segmentation effect. The skin at the focal site of anal abscess was obviously thickened, and it was simple and accurate to use CT images in the diagnosis of patients with perianal abscesses, which could effectively locate the lesion and clarify the relationship between the lesion and the surrounding structure.

scholarly journals Deep Learning in Laparoscopic Colorectal Carcinoma Surgery under Magnetic Resonance Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shuguang Pan ◽  
Wei Tang ◽  
Tiejun Zhou ◽  
Wei Luo
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Laparoscopic Surgery ◽  
Magnetic Resonance ◽  
Colorectal Carcinoma ◽  
Control Group ◽  
Resonance Imaging ◽  
Neural Network Algorithm ◽  
Experimental Group

This study aimed to explore the application effect of magnetic resonance imaging (MRI) based on deep learning in laparoscopic surgery for colorectal carcinoma (CRC). 40 patients with CRC who were diagnosed and required laparoscopic surgery were selected in the research. The MRI scan images of all patients were processed based on the convolutional neural network algorithm. The MRI images before and after treatment were set as the control group and the experimental group, respectively. The consistency of MRI results with laparoscopic and postoperative pathological biopsy results was observed. Through the comparative analysis of the research results, in terms of consistency with the surgical plane, the assessment results of the experimental group were more consistent than those of the control group and direct observation under laparoscopy, and the difference was statistically significant ( P < 0.05 ). In terms of tumor T staging, the consistency between the experimental group and pathological biopsy results was superior to that of the control group, with considerable difference ( P < 0.05 ). In conclusion, practically speaking, the application of MR images based on convolutional neural network algorithm in laparoscopic CRC surgery was better than conventional MRI technology. However, the research was a small-scale pathological study, which was not very representative.

scholarly journals Application of Magnetic Resonance Image Intelligent Data Acquisition in Ovarian Cancer (Preprint)

2020 ◽  
Author(s):  
Tingting Liang ◽  
Yanlei Dong ◽  
Xinrui Zhao ◽  
Lu Wang ◽  
Hui Xu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Magnetic Resonance ◽  
Epithelial Cells ◽  
Data Acquisition ◽  
Cell Lines ◽  
P53 Protein ◽  
Control Group ◽  
Ovarian Epithelial Cells ◽  
Intelligent Data Acquisition ◽  
Experimental Group

UNSTRUCTURED As a diagnostic method with no radiation, high resolution of soft tissue, and different imaging methods, Magnetic Resonance Imaging (MRI) intelligent data acquisition is more and more widely used in the examination of abdominal, pelvic, and other organ lesions. In order to study the diagnostic effect of multi-mode magnetic resonance intelligent data acquisition on ovarian cancer and the ovarian cancer model modified based on p53-/-+Myc+ASAP1 gene, NSG mice were selected as experimental subjects in this study. 293FT cell lines packaging p53, Myc, and ASAP1(ArfGAP with SH3 domain, Ankyrin repeat and PH domain 1) recombinant lentivirus were inoculated into mouse ovarian epithelial cells to construct mouse ovarian epithelial cell tumor cell lines and their performance was analyzed. According to the different injection cell lines, they were divided into the experimental group and the control group. Tumor samples were collected and the mice were analyzed using immunofluorescence staining and MRI. The results showed that, in the detection of protein expression in genetically modified cell lines, for p53-/-+Myc+ASAP1 fully modified cell lines, the high expression of ASAP1 and Myc functional proteins was detected after the lentivirus containing p53-/-, ASAP1, and Myc were introduced into mouse ovarian epithelial cells, while the expression of p53 protein decreased significantly; after inoculation into mice, it was found that the expression of ASAP1 protein and Myc protein in the experimental group was significantly higher than that in the control group, while the expression of p53 protein in the experimental group was significantly lower than that in the control group, with significant statistical difference; further MRI diagnosis of two groups of mice showed that the ADC (Apparent dispersion coefficient) value of the experimental group was significantly higher than the control group, there were statistically significant differences. Therefore, it was found that p53 gene expression was down-regulated and Myc and ASAPl genes were overexpressed in the tumor tissues and tumor cells formed, and tumor formation differences between the two groups of mice could be obviously found after MRI intelligent data acquisition, which provided experimental basis for early diagnosis of breast cancer in the later clinical stage.

scholarly journals Reclassification of Ischemic Stroke Etiological Subtypes on the Basis of High-Risk Nonstenosing Carotid Plaque

Stroke ◽  
2020 ◽  
Vol 51 (2) ◽  
pp. 504-510 ◽  
Author(s):  
Hooman Kamel ◽  
Babak B. Navi ◽  
Alexander E. Merkler ◽  
Hediyeh Baradaran ◽  
Iván Díaz ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
High Risk ◽  
Magnetic Resonance ◽  
Magnetic Resonance Angiography ◽  
Acute Stroke ◽  
Carotid Plaque ◽  
Brain Infarction ◽  
Intraplaque Hemorrhage ◽  
Large Artery ◽  
Stroke Subtypes

Background and Purpose— Carotid artery plaque with <50% luminal stenosis may be an underappreciated stroke mechanism. We assessed how many stroke causes might be reclassified after accounting for nonstenosing plaques with high-risk features. Methods— We included patients enrolled in the Cornell Acute Stroke Academic Registry from 2011 to 2015 who had anterior circulation infarction, magnetic resonance imaging of the brain, and magnetic resonance angiography of the neck. High-risk plaque was identified by intraplaque hemorrhage ascertained from routine neck magnetic resonance angiography studies using validated methods. Infarct location was determined from diffusion-weighted imaging. Intraplaque hemorrhage and infarct location were assessed separately in a blinded fashion by a neuroradiologist. We used the McNemar test for matched data to compare the prevalence of intraplaque hemorrhage ipsilateral versus contralateral to brain infarction. We reclassified stroke subtypes by including large-artery atherosclerosis as a cause if there was intraplaque hemorrhage ipsilateral to brain infarction, regardless of the degree of stenosis. Results— Among the 1721 acute ischemic stroke patients registered in the Cornell Acute Stroke Academic Registry from 2011 to 2015, 579 were eligible for this analysis. High-risk plaque was more common ipsilateral versus contralateral to brain infarction in large-artery atherosclerotic (risk ratio [RR], 3.7 [95% CI, 2.2–6.1]), cryptogenic (RR, 2.1 [95% CI, 1.4–3.1]), and cardioembolic strokes (RR, 1.7 [95% CI, 1.1–2.4]). There were nonsignificant ipsilateral-contralateral differences in high-risk plaque among lacunar strokes (RR, 1.2 [95% CI, 0.4–3.5]) and strokes of other determined cause (RR, 1.5 [95% CI, 0.7–3.3]). After accounting for ipsilateral high-risk plaque, 88 (15.2%) patients were reclassified: 38 (22.6%) cardioembolic to multiple potential etiologies, 6 (8.5%) lacunar to multiple, 3 (15.8%) other determined cause to multiple, and 41 (20.8%) cryptogenic to large-artery atherosclerosis. Conclusions— High-risk carotid plaque was more prevalent ipsilateral to brain infarction across several ischemic stroke subtypes. Accounting for such plaques may reclassify the etiologies of up to 15% of cases in our sample.

scholarly journals EFFICACY OF FUNCTIONAL NEUROMUSCULAR ELECTRICAL STIMULATION (FNMES) IN THE IMPROVEMENT OF HAND FUNCTIONS IN ACUTE STROKE SURVIVALS

2012 ◽  
Vol 02 (04) ◽  
pp. 16-21
Author(s):  
Mohamed Faisal C. K. ◽  
Priyabandani Neha Om Prakash ◽  
Ajith S.
Keyword(s):  
Electrical Stimulation ◽  
Acute Stroke ◽  
Hand Function ◽  
Neuromuscular Electrical Stimulation ◽  
Control Group ◽  
Stroke Patients ◽  
Hand Functions ◽  
Conventional Physiotherapy ◽  
Block Test ◽  
Experimental Group

AbstractStroke is a worldwide health problem. Hand function is one of the important factors which are affected in stroke. Stroke patients are usually given a conventional physiotherapy but if an additional FNMES is given it might show better improvement. By keeping these facts in view, the present study aims at evaluating and comparing the efficacy of conventional physiotherapy and adding FNMES will make any better outcome in the acute stroke survivals. The subjects were randomly assigned to any of the two groups; control group consisted of 15 subjects who received only conventional therapy for 4 weeks and experimental group consisting of 15 subjects who received an additional FNMES along with conventional physiotherapy for 4 weeks. The hand function was assessed on day 1 and to know the recovery, at the end of four weeks of intervention with the help of action research arm test (ARAT) and box and block test (BBT). At the end of 4 weeks of intervention both the groups showed significant improvements. On ARAT, control group showed a mean of 10.2000 whereas, experimental group showed mean of 20.8000 with p = 0.001 (p ≤ 0.05) and on BBT, the control group showed a mean of 21.666 and experimental group showed 30.933 with p = 0.41 (p ≤ S 0.05). Therefore the study concludes that, though there was improvement in both the groups, the experimental group who received an additional FNMES along with conventional physiotherapy showed better improvement in hand functions in the acute stroke survivals.

scholarly journals Early path nursing on neurological function recovery of cerebral infarction

2019 ◽  
Vol 10 (1) ◽  
pp. 160-163
Author(s):  
Ling Chen ◽  
Zhena Han ◽  
Junjie Gu
Keyword(s):  
Cerebral Infarction ◽  
Conventional Treatment ◽  
Neurological Function ◽  
Early Rehabilitation ◽  
Control Group ◽  
P Value ◽  
Treatment Methods ◽  
Rehabilitation Care ◽  
Function Recovery ◽  
Experimental Group

Abstract Purpose to study the application of path type early rehabilitation nursing in the nursing of patients with cerebral infarction and to explore its impact on the recovery of neurological function. Methods Patients with acute cerebral infarction in our hospital were randomly divided into two groups. The control group used conventional treatment methods. The experimental group used path type early rehabilitation care based on conventional treatment methods and observed the curative effect. ResultsThe NIHSS scores in the experimental group were significantly lower than those in the control group, and the P value was less than 0.05, which was statistically significant. Conclusion Path type early rehabilitation nursing has a positive effect on the treatment of patients with cerebral infarction, which contributes to the recovery of neurological function of patients and is worthy of promotion in treatment.

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