scholarly journals Deep Learning Algorithm Based on Analysis the Effect of Posterior Cervical Vertebral Canal Decompression Angioplasty in the Treatment of Ossification of Posterior Longitudinal Ligament of Cervical Spine by CT Image

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Yang Liu ◽  
Jianjun Kong
Keyword(s):  
Deep Learning ◽  
Cervical Spine ◽  
Spinal Stenosis ◽  
Learning Algorithm ◽  
Posterior Longitudinal Ligament ◽  
Ct Image ◽  
Hyperextension Injury ◽  
Vertebral Canal ◽  
Deep Learning Algorithm ◽  
Better Than

Original Article Deep learning algorithm based on analyzing the effect of posterior cervical vertebral canal decompression angioplastyin the treatment of ossification of posterior longitudinalligament of cervical spine by CT image Yang Liu1, Jianjun Kong2 ABSTRACTObjective: The paper uses the convolutional neural network algorithm in the deep learning algorithm to explore the therapeutic effect of surgical treatment of hyperextension injuries associated with ossification of the posterior longitudinal ligament of the cervical spine. Methods: In this retrospectively analyzed study 27 patients with hyperextension injury of the posterior longitudinal ligament of the cervical spine were selected from our hospital between August 2018 to July 2020. It included 21 males and 6 females; aged 36-79 years, with an average of 55.9 years. Results: Follow-up time of patients was 3-39 months, with an average of 17.4 months. The JOA score after surgery was significantly better than that before surgery (P﹤0.01), which was statistically significant; the improvement of JOA in patients undergoing anterior therapy was better than that in patients undergoing posterior therapy, which was statistically significant; the JOA improved in patients with minor violent injuries. The situation is significantly better than severe violent injuries, with statistical significance. The rate of postoperative JOA improvement was significantly correlated with the degree of nerve function retention of the injured spinal cord before surgery (P﹤0.01), and there was no significant correlation between the degree of spinal stenosis caused by ossification and the postoperative JOA improvement of patients.Conclusion: Convolutional neural network algorithm in the deep learning algorithm based on the cervical spine posterior longitudinal ligament ossification hyperextension injury was significantly improved after surgery. The less preoperative neurological damage, the postoperative neurological function, the degree of improvement, there was no significant correlation between the degree of spinal stenosis and the improvement of postoperative spinal cord function. For patients with ossification of the posterior longitudinal ligament, if there are neurological symptoms, early surgical treatment is recommended to relieve the compression, so as to prevent irreversible neurological damage caused by trauma. KEYWORDS: Cervical spine, Ossification of posterior longitudinal ligament, Hyperextension injury, Spinal stenosis rate, JOA improvement rate. doi: https://doi.org/10.12669/pjms.37.6-WIT.4857How to cite this:Liu Y, Kong J. Deep learning algorithm based on analyzing the effect of posterior cervical vertebral canal decompression angioplasty in the treatment of ossification of posterior longitudinal ligament of cervical spine by CT image. Pak J Med Sci. 2021;37(6):1630-1635. doi: https://doi.org/10.12669/pjms.37.6-WIT.4857 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Evaluation of Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction Based on CT Features of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Zhang ◽  
Yang Wang
Keyword(s):  
Deep Learning ◽  
Airway Obstruction ◽  
Airway Inflammation ◽  
Maintenance Treatment ◽  
Learning Algorithm ◽  
Small Airway ◽  
Imaging Features ◽  
Ct Image ◽  
Deep Learning Algorithm ◽  
Small Airway Obstruction

This study was aimed at exploring the treatment of asthma children with small airway obstruction in CT imaging features of deep learning and glucocorticoid. A total of 145 patients meeting the requirements in hospital were included in this study, and they were randomly assigned to receive aerosolized glucocorticoid ( n = 45 ), aerosolized glucocorticoid combined with bronchodilator ( n = 50 ), or oral steroids ( n = 50 ) for 4 weeks after discharge. The lung function and fractional exhaled nitric oxide (FENO) indexes of the three groups were measured, respectively, and then the effective rates were compared to evaluate the clinical efficacy of glucocorticoids with different administration methods and combined medications in the short-term maintenance treatment after acute exacerbation of asthma. Deep learning algorithm was used for CT image segmentation. The CT image is sent to the workbench for processing on the workbench, and then the convolution operation is performed on each input pixel point during the image processing. After 4 weeks of maintenance treatment, FEF50 %, FEF75 %, and MMEF75/25 increased significantly, and FENO decreased significantly ( P < 0.01 ). The improvement results of FEF50 %, FEF75 %, MMEF75/25, and FENO after maintenance treatment were as follows: the oral hormone group was the most effective, followed by the combined atomization inhalation group, and the hormone atomization inhalation group was the least effective. The differences among them were statistically significant ( P < 0.05 ). The accuracy of artificial intelligence segmentation algorithm was 81%. All the hormones were more effective than local medication in the treatment of small airway function and airway inflammation. In the treatment of aerosol inhalation, the hormone combined with bronchiectasis drug was the most effective in improving small airway obstruction and reducing airway inflammation compared with single drug inhalation. Deep learning CT images are simple, noninvasive, and intuitively observe lung changes in asthma with small airway functional obstruction. Asthma with small airway functional obstruction has high clinical diagnosis and evaluation value.

scholarly journals Research on Classification Method of Maize Seed Defect Based on Machine Vision

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Sheng Huang ◽  
Xiaofei Fan ◽  
Lei Sun ◽  
Yanlu Shen ◽  
Xuesong Suo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Heat Map ◽  
Deep Learning Algorithm ◽  
Quality Classification ◽  
Visualization Technology ◽  
Better Than

Traditionally, the classification of seed defects mainly relies on the characteristics of color, shape, and texture. This method requires repeated extraction of a large amount of feature information, which is not efficiently used in detection. In recent years, deep learning has performed well in the field of image recognition. We introduced convolutional neural networks (CNNs) and transfer learning into the quality classification of seeds and compared them with traditional machine learning algorithms. Experiments showed that deep learning algorithm was significantly better than the machine learning algorithm with an accuracy of 95% (GoogLeNet) vs. 79.2% (SURF+SVM). We used three classifiers in GoogLeNet to demonstrate that network accuracy increases as the depth of the network increases. We used the visualization technology to obtain the feature map of each layer of the network in CNNs and used the heat map to represent the probability distribution of the inference results. As an end-to-end network, CNNs can be easily applied for automated seed manufacturing.

scholarly journals Towards deep learning with segregated dendrites

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Jordan Guerguiev ◽  
Timothy P Lillicrap ◽  
Blake A Richards
Keyword(s):  
Deep Learning ◽  
Pyramidal Neurons ◽  
Learning Algorithm ◽  
Sensory Information ◽  
Single Layer ◽  
Higher Order ◽  
Deep Learning Algorithm ◽  
Single Layer Network ◽  
Better Than ◽  
Neocortical Pyramidal Neurons

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

scholarly journals Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaojie Fan ◽  
Xiaoyu Zhang ◽  
Zibo Zhang ◽  
Yifang Jiang
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Spinal Metastasis ◽  
Ct Images ◽  
Ct Image ◽  
Spectral Ct ◽  
Deep Learning Algorithm ◽  
Noise Ratio

In this study, deep learning algorithm-based energy/spectral computed tomography (CT) for the spinal metastasis from lung cancer was used. A dilated convolutional U-Net model (DC-U-Net model) was first proposed, which was used to segment the energy/spectral CT image of patients with the spinal metastasis from lung cancer. Subsequently, energy/spectral CT images under different energy levels were collected for the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) comparison. It was found the learning rate of the model decreased exponentially as the number of training increased, with the lung contour segmented out of the image. Under 40–65 keV, the CT value of bone metastasis from lung cancer decreased with increasing energy, as with the average rank sum test result. The SNR and CNR values were the highest under 60 keV. The detection rate of the deep learning algorithm below 60 keV was 81.41%, and that of professional doctors was 77.56%. The detection rate of the deep learning algorithm below 140 keV was 66.03%, and that of professional doctors was 64.74%. In conclusion, the DC-U-Net model demonstrates better segmentation effects versus the convolutional neutral networ k (CNN), with the lung contour segmented. Further, a higher energy level leads to worse segmentation effects on the energy/spectral CT image.

scholarly journals CT Image Feature Diagnosis on the Basis of Deep Learning Algorithm for Preoperative Patients and Complications of Transcatheter Aortic Valve Implantation

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiong Zheng ◽  
Zhang Qian ◽  
Xiaofang Wang ◽  
Zhen Zhang ◽  
Lei Liu
Keyword(s):  
Deep Learning ◽  
Aortic Valve ◽  
Learning Algorithm ◽  
Average Diameter ◽  
Ct Image ◽  
Transcatheter Aortic Valve ◽  
Deep Learning Algorithm ◽  
Aortic Valve Implantation ◽  
Valve Implantation ◽  
Short Diameter

This work was aimed to explore the role of CT angiography information provided by deep learning algorithm in the diagnosis and complications of the disease focusing on congenital aortic valve disease and severe aortic valve stenosis. 120 patients who underwent ultrasound cardiography for aortic stenosis and underwent transcatheter aortic valve implantation (TAVI) in hospital were selected as the research objects. Patients received CT examination of deep learning algorithm within one week. The measurement methods were long and short diameter method, area method, and perimeter method. The deep learning algorithm was used to measure the long and short diameter, area, and perimeter of the target area before CT image processing. The results showed that the average diameter of long and short diameter measurement was 95% CI (0.84, 0.92), the average diameter of perimeter measurement was 95% CI (0.68, 0.87), and the average diameter of area measurement was 95% CI (0.72, 0.91). Among the 52 patients, 35 cases were hypertension (67%), 13 cases were diabetes (25%), 6 cases were chronic renal insufficiency (Cr > 2 mg/dL) (11%) (2 cases were treated with hemodialysis, 3.8%), 11 patients had chronic pulmonary disease (21%), 9 patients had cerebrovascular disease (17.3%) and atrial flutter and atrial fibrillation. Deep learning can achieve excellent results in CT image processing, and it was of great significance for the diagnosis of TAVI patients, improving the success rate of treatment and the prognosis of patients.

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