scholarly journals Back Propagation Neural Network-Based Magnetic Resonance Imaging Image Features in Treating Intestinal Obstruction in Digestive Tract Diseases with Chengqi Decoction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yongfeng Li ◽  
Kaina Wang ◽  
Li Gao ◽  
Xiaojun Lu
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Intestinal Obstruction ◽  
Back Propagation ◽  
Block Size ◽  
Back Propagation Neural Network ◽  
Image Features ◽  
Resonance Imaging ◽  
Mri Scans

This study was to explore the adoption effect of magnetic resonance imaging (MRI) image features based on back propagation neural network (BPNN) in evaluating the curative effect of Chengqi Decoction (CD) for intestinal obstruction (ileus), so as to evaluate the clinical adoption value of this algorithm. Ninety patients with ileus were recruited, and the patients were treated with CD and underwent MRI scans of the lower abdomen. A BPNN model was fabricated and applied to segment the MRI images of patients and identify the lesion. As a result, when the overlap step was 16 and the block size was 32 × 32, the running time of the BPNN algorithm was the shortest. The segmentation accuracy was the highest if there were two hidden layer (HL) nodes, reaching 97.3%. The recognition rates of small intestinal stromal tumor (SIST), colon cancer, adhesive ileus, and volvulus of MRI images segmented by the algorithm were 91.5%, 88.33%, 90.3%, and 88.9%, respectively, which were greatly superior to those of manual interpretation ( P < 0.05 ). After the intervention of CD, the percentages of patients with ileus that were cured, markedly effective, effective, and ineffective were 65.38%, 23.16%, 5.38%, and 6.08%, respectively. The cure rate after intervention of CD (65.38%) was much higher in contrast to that before intervention (13.25%) ( P < 0.05 ). In short, CD showed a good therapeutic effect on ileus and can effectively improve the prognosis of patients. In addition, MRI images based on BPNN showed a good diagnostic effect on ileus, and it was worth applying to clinical diagnosis.

scholarly journals Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yang Gao ◽  
Xiong Xiao ◽  
Bangcheng Han ◽  
Guilin Li ◽  
Xiaolin Ning ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Tumor Recurrence ◽  
Radiation Necrosis ◽  
Resonance Imaging ◽  
Glioma Recurrence ◽  
Mri Scans

BACKGROUND The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. OBJECTIVE This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. METHODS In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. RESULTS DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (<i>P</i>=.02 in sensitivity and <i>P</i>&lt;.001 in specificity and accuracy). CONCLUSIONS Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

scholarly journals Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jong Bin Bae ◽  
Subin Lee ◽  
Wonmo Jung ◽  
Sejin Park ◽  
Weonjin Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Medial Temporal Lobe ◽  
Processing Time ◽  
Resonance Imaging ◽  
Mri Scans

AbstractThe classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.

scholarly journals Deep Learning Methodology for Differentiating Glioma Recurrence From Radiation Necrosis Using Multimodal Magnetic Resonance Imaging: Algorithm Development and Validation

10.2196/19805 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19805
Author(s):  
Yang Gao ◽  
Xiong Xiao ◽  
Bangcheng Han ◽  
Guilin Li ◽  
Xiaolin Ning ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Tumor Recurrence ◽  
Radiation Necrosis ◽  
Resonance Imaging ◽  
Glioma Recurrence ◽  
Mri Scans

Background The radiological differential diagnosis between tumor recurrence and radiation-induced necrosis (ie, pseudoprogression) is of paramount importance in the management of glioma patients. Objective This research aims to develop a deep learning methodology for automated differentiation of tumor recurrence from radiation necrosis based on routine magnetic resonance imaging (MRI) scans. Methods In this retrospective study, 146 patients who underwent radiation therapy after glioma resection and presented with suspected recurrent lesions at the follow-up MRI examination were selected for analysis. Routine MRI scans were acquired from each patient, including T1, T2, and gadolinium-contrast-enhanced T1 sequences. Of those cases, 96 (65.8%) were confirmed as glioma recurrence on postsurgical pathological examination, while 50 (34.2%) were diagnosed as necrosis. A light-weighted deep neural network (DNN) (ie, efficient radionecrosis neural network [ERN-Net]) was proposed to learn radiological features of gliomas and necrosis from MRI scans. Sensitivity, specificity, accuracy, and area under the curve (AUC) were used to evaluate performance of the model in both image-wise and subject-wise classifications. Preoperative diagnostic performance of the model was also compared to that of the state-of-the-art DNN models and five experienced neurosurgeons. Results DNN models based on multimodal MRI outperformed single-modal models. ERN-Net achieved the highest AUC in both image-wise (0.915) and subject-wise (0.958) classification tasks. The evaluated DNN models achieved an average sensitivity of 0.947 (SD 0.033), specificity of 0.817 (SD 0.075), and accuracy of 0.903 (SD 0.026), which were significantly better than the tested neurosurgeons (P=.02 in sensitivity and P<.001 in specificity and accuracy). Conclusions Deep learning offers a useful computational tool for the differential diagnosis between recurrent gliomas and necrosis. The proposed ERN-Net model, a simple and effective DNN model, achieved excellent performance on routine MRI scans and showed a high clinical applicability.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Magnetic resonance imaging (MRI) scans in patients with dorsal root ganglion stimulation

Pain Practice ◽  
2021 ◽  
Author(s):  
Marco Reining ◽  
Dirk Winkler ◽  
Joachim Boettcher ◽  
Juergen Meixensberger ◽  
Michael Kretzschmar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Dorsal Root Ganglion ◽  
Dorsal Root ◽  
Resonance Imaging ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging features of multiple duodenal lipomas: a rare cause of intestinal obstruction

2012 ◽  
Vol 30 (8) ◽  
pp. 676-679 ◽  
Author(s):  
Jelena Djokić Kovač ◽  
Marija Kratovac Dunjić ◽  
Miloš Bjelović ◽  
Bojan Banko ◽  
Gordana Lilić ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Intestinal Obstruction ◽  
Imaging Features ◽  
Resonance Imaging

scholarly journals Predictors of Spondylolysis on Magnetic Resonance Imaging in Adolescent Athletes With Low Back Pain

2021 ◽  
Vol 9 (4) ◽  
pp. 232596712199546
Author(s):  
Takuji Yokoe ◽  
Takuya Tajima ◽  
Hiroshi Sugimura ◽  
Shinichirou Kubo ◽  
Shotarou Nozaki ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Low Back Pain ◽  
Back Pain ◽  
Magnetic Resonance ◽  
Neurological Symptoms ◽  
Low Back ◽  
Resonance Imaging ◽  
Advanced Imaging ◽  
Adolescent Athletes ◽  
Mri Scans

Background: Spondylolysis and undiagnosed mechanical low back pain (UMLBP) are the main causes of low back pain (LBP) in adolescent athletes. No studies have evaluated the difference in clinical and radiographic factors between these 2 conditions. Furthermore, it remains unclear which adolescent athletes with LBP should undergo advanced imaging examination for spondylolysis. Purpose: To compare the clinical and radiographic factors of adolescent athletes with spondylolysis and UMLBP who did not have neurological symptoms or findings before magnetic resonance imaging (MRI) evaluation and to determine the predictors of spondylolysis findings on MRI. Study Design: Cohort study, Level of evidence, 3. Methods: The study population included 122 adolescent athletes aged 11 to 18 years who had LBP without neurological symptoms or findings and who underwent MRI. Of these participants, 75 were ultimately diagnosed with spondylolysis, and 47 were diagnosed with UMLBP. Clinical factors and the following radiographic parameters were compared between the 2 groups: spina bifida occulta, lumbar lordosis (LL) angle, and the ratio of the interfacet distance of L1 to that of L5 (L1:L5 ratio, %). A logistic regression analysis was performed to evaluate independent predictors of spondylolysis on MRI scans. Results: Significantly more athletes with spondylolysis were male (82.7% vs 48.9%; P < .001), had a greater LL angle (22.8° ± 8.1° vs 19.3° ± 8.5°; P = .02), and had a higher L1:L5 ratio (67.4% ± 6.3% vs 63.4% ± 6.6%; P = .001) versus athletes with UMLBP. A multivariate analysis revealed that male sex (odds ratio [OR], 4.66; P < .001) and an L1:L5 ratio of >65% (OR, 3.48; P = .003) were independent predictors of positive findings of spondylolysis on MRI scans. Conclusion: The study findings indicated that sex and the L1:L5 ratio are important indicators for whether to perform MRI as an advanced imaging examination for adolescent athletes with LBP who have no neurological symptoms and findings.

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