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 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 Deep Learning-Based CT Image Characteristics and Postoperative Anal Function Restoration for Patients with Complex Anal Fistula

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lingling Han ◽  
Yue Chen ◽  
Weidong Cheng ◽  
He Bai ◽  
Jian Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Anal Fistula ◽  
Postoperative Recurrence ◽  
Ct Images ◽  
Control Group ◽  
Research Subjects ◽  
Anal Function ◽  
Imaging Results ◽  
Experimental Group

Objective. This study aimed to optimize the CT images of anal fistula patients using a convolutional neural network (CNN) algorithm to investigate the anal function recovery. Methods. 57 patients with complex anal fistulas admitted to our hospital from January 2020 to February 2021 were selected as research subjects. Of them, CT images of 34 cases were processed using the deep learning neural network, defined as the experimental group, and the remaining unprocessed 23 cases were in the control group. Whether to process CT images depended on the patient’s own wish. The imaging results were compared with the results observed during the surgery. Results. It was found that, in the experimental group, the images were clearer, with DSC = 0.89, precision = 0.98, and recall = 0.87, indicating that the processing effects were good; that the CT imaging results in the experimental group were more consistent with those observed during the surgery, and the difference was notable ( P < 0.05 ). Furthermore, the experimental group had lower RP (mmHg), AMCP (mmHg) scores, and postoperative recurrence rate, with notable differences noted ( P < 0.05 ). Conclusion. CT images processed by deep learning are clearer, leading to higher accuracy of preoperative diagnosis, which is suggested in clinics.

scholarly journals Computed Tomography Image Feature under Intelligent Algorithms in Diagnosing the Effect of Humanized Nursing on Neuroendocrine Hormones in Patients with Primary Liver Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiujie Wang ◽  
Lin Liu ◽  
Na Ma ◽  
Xinxin Zhao
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Primary Liver Cancer ◽  
Ct Images ◽  
Image Feature ◽  
Control Group ◽  
Intelligent Algorithms ◽  
Residual Coding ◽  
Computed Tomography Image ◽  
Experimental Group

This study was to explore the application value of computed tomography (CT) images processed by intelligent algorithm denoising in the evaluation of humanized nursing in postoperative neuroendocrine hormone changes in patients with primary liver cancer (PLC). In this study, a simple-structured recursive residual coding and decoding (RRCD) algorithm was constructed on the basis of residual network, which can effectively remove artifacts and noise in CT images and can also restore image details and lesion features well. In addition, 60 postoperative patients with primary liver cancer were collected and divided into routine nursing control group (30 cases) and humanized nursing experimental group (30 cases). After a period of nursing, CT images based on intelligent algorithms were evaluated by determining the hormone content. The results showed that the focal necrosis rate (FNR) of the experimental group was 6%. The adrenocorticotropic hormone (ACTH) levels of 6 and 15 days after admission (T3 and T4) were 41.25 ± 3.81 pg/mL and 19.55 ± 1.72 pg/mL, respectively. The cortisol levels of days 6, 15, and 30 after admission (T3, T4, and T5) were 424.86 ± 16.82 nmol/L, 277.98 ± 14.36 nmol/L, and 241.53 ± 13.27 nmol/L, respectively. Estradiol levels were 53.48 ± 11.19 pg/mL, 41.64 ± 9.28 pg/mL, and 30.59 ± 8.16 pg/mL, respectively. Testosterone levels were 2.18 ± 1.14 ng/mL, 1.78 ± 1.03 ng/mL, and 1.42 ± 0.69 ng/mL, respectively. Self-Rating Anxiety Scale (SAS) scores were 40.24 ± 5.81 points, 36.55 ± 5.02 points, and 32.53 ± 4.8 points, respectively. There were 24 cases, 27 cases, 23 cases, and 21 patients who followed no smoking and drinking, taking medication on time, diet control, and self-monitoring. The scores of physical function, self-cognition, emotional function, and social function were 62.59 ± 6.82 points, 69.26 ± 8.14 points, 73.89 ± 6.35 points, and 66.88 ± 7.04 points, which were better than those of the control group in all aspects ( P < 0.05 ). In short, the humanized nursing course can enhance the compliance of the patients after the surgery, improve the quality of life, and inhibit the anxiety and depression of the patients, so it showed a positive effect on the neuroendocrine hormones and the prognosis of the patients.

scholarly journals Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xueling Wang ◽  
Xianmin Meng ◽  
Shu Yan
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Oral Lesion ◽  
Cone Beam ◽  
Control Group ◽  
Oral Lesions ◽  
Manual Segmentation ◽  
Blank Group ◽  
And Control ◽  
Experimental Group

This paper aimed to study the adoption of deep learning (DL) algorithm of oral lesions for segmentation of cone-beam computed tomography (CBCT) images. 90 patients with oral lesions were taken as research subjects, and they were grouped into blank, control, and experimental groups, whose images were treated by the manual segmentation method, threshold segmentation algorithm, and full convolutional neural network (FCNN) DL algorithm, respectively. Then, effects of different methods on oral lesion CBCT image recognition and segmentation were analyzed. The results showed that there was no substantial difference in the number of patients with different types of oral lesions among three groups ( P > 0.05 ). The accuracy of lesion segmentation in the experimental group was as high as 98.3%, while those of the blank group and control group were 78.4% and 62.1%, respectively. The accuracy of segmentation of CBCT images in the blank group and control group was considerably inferior to the experimental group ( P < 0.05 ). The segmentation effect on the lesion and the lesion model in the experimental group and control group was evidently superior to the blank group ( P < 0.05 ). In short, the image segmentation accuracy of the FCNN DL method was better than the traditional manual segmentation and threshold segmentation algorithms. Applying the DL segmentation algorithm to CBCT images of oral lesions can accurately identify and segment the lesions.

A deep Residual U-Net algorithm for automatic detection and quantification of ascites on the abdominopelvic computed tomography acquired in the emergency department (Preprint)

2021 ◽  
Author(s):  
Hoon Ko ◽  
Jimi Huh ◽  
Kyung Won Kim ◽  
Heewon Chung ◽  
Yousun Ko ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Automatic Detection ◽  
High Specificity ◽  
Ct Images ◽  
Free Fluid ◽  
Detection Accuracy ◽  
Abdominopelvic Ct ◽  
Segmentation Accuracy ◽  
Detection And Quantification

BACKGROUND Detection and quantification of intraabdominal free fluid (i.e., ascites) on computed tomography (CT) are essential processes to find emergent or urgent conditions in patients. In an emergent department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS 2D deep learning models (DLMs) based on a deep residual U-Net, U-Net, bi-directional U-Net, and recurrent residual U-net were developed to segment areas of ascites on an abdominopelvic CT. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and non-ascites images. The AI algorithms were trained using 6,337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1,635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS The segmentation accuracy was the highest in the deep residual U-Net with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bi-directional U-Net, and recurrent residual U-net (mIoU values 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest in the deep residual U-net (0.96), followed by U-Net, bi-directional U-net, and recurrent residual U-net (0.90, 0.88, and 0.82, respectively). The deep residual U-net also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS We propose the deep residual U-net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

scholarly journals A multi-label classification model for full slice brain computerised tomography image

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Jianqiang Li ◽  
Guanghui Fu ◽  
Yueda Chen ◽  
Pengzhi Li ◽  
Bo Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Image Features ◽  
Classification Model ◽  
Brain Diseases ◽  
Ct Scans ◽  
Computerised Tomography ◽  
Brain Ct ◽  
Proposed Model ◽  
The Brain

Abstract Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.

scholarly journals Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuehong Zhou
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Ultrasound Examination ◽  
Ultrasound Image ◽  
Control Group ◽  
Test Results ◽  
Experimental Group ◽  
Central Nervous System Malformation

This study was to explore the application of deep learning neural network (DLNN) algorithms to identify and optimize the ultrasound image so as to analyze the effect and value in diagnosis of fetal central nervous system malformation (CNSM). 63 pregnant women who were gated in the hospital were suspected of being fetal CNSM and were selected as the research objects. The ultrasound images were reserved in duplicate, and one group was defined as the control group without any processing, and images in the experimental group were processed with the convolutional neural network (CNN) algorithm to identify and optimize. The ultrasound examination results and the pathological test results before, during, and after the pregnancy were observed and compared. The results showed that the test results in the experimental group were closer to the postpartum ultrasound and the results of the pathological result, but the results in both groups showed no statistical difference in contrast to the postpartum results in terms of similarity ( P > 0.05 ). In the same pregnancy stage, the ultrasound examination results of the experimental group were higher than those in the control group, and the contrast was statistically significant ( P < 0.05 ); in the different pregnancy stages, the ultrasound examination results in the second trimester were more close to the postpartum examination results, showing statistically obvious difference ( P < 0.05 ). In conclusion, ultrasonic image based on deep learning was higher in CNSM inspection; and ultrasonic technology had to be improved for the examination in different pregnancy stages, and the accuracy of the examination results is improved. However, the amount of data in this study was too small, so the representative was not high enough, which would be improved.

scholarly journals INSTRUMENTAL AND LABORATORY STUDY OF JAW MINERALIZATION LEVEL IN FEMALE PATIENTS WITH ESTROGEN IMBALANCE

2020 ◽  
Vol 10 (02) ◽  
pp. 819-827
Author(s):  
Leonid D. Chulak, Oleksandr S. Barylo, Nataliia G. Gadzhula Georgij M. Varava, Ruslan L. Furman*, Kyryl V. Agafonov
Keyword(s):  
Computed Tomography ◽  
Acid Phosphatase ◽  
Control Group ◽  
Bone Demineralization ◽  
Female Patients ◽  
Carious Lesions ◽  
Group 2 ◽  
Tomography Method ◽  
Experimental Group ◽  
Group 1

Introduction: Prevention of dental caries and non-carious lesions associated withdemineralizing effect of estrogen imbalance in perimenopause women isrelevant problem in modern dentistry.The aim: To improve the effectiveness of prevention of caries and non-cariouslesions in female patients with estrogen imbalance.Materials and methods: 3 groups of patients were selected for the study (20women in each): the patients of the control group receiving no additionaltreatment, the patients of experimental group 1 receiving general therapy withcomplex calcium supplements, the patients of experimental group 2 receivinggeneral therapy of complex calcium medicines combined withaminobisphosphonates.To quantify bone density of the jaws by computed tomography method, X-rayattenuation scale, called the Hounsfield scale was used. Bone tissues of the jawswere studied using 3D cone beam computed tomography scanner PlanmecaProMax. Planmeca Romexis® software was used for data processing andinterpretation. Estrogen level was estimated in all patients to confirm itsimbalance. The level of serum acid phosphatase was determined for indirectstudy of osteoporosis degree.Results: At the beginning of the research, significant bone demineralization andincreased level of acidic phosphatase were observed in all groups of women. In6 months of treatment the Hounsfield index and the level of acidic phosphatasein the control group were almost unchanged; in experimental group 1 – 1571±44 HU and 4.93 ± 0.26 IU/l; in experimental group 2 – 1701±48 HU and 2.43± 0.18 IU/l.Conclusions: Combination therapy consisting of calcium phosphate withvitamin D3 and aminobisphosphonates used for 6 months increased bonemineralization by 13.05% and reduced the activity of acid phosphatase by64.2%. This therapeutic complex results in decreased destruction of bone tissueand enhances remineralization of bone tissue and dental hard tissues.Key words: teeth mineralization, osteoporosis, estrogen dysfunction, Hounsfieldindex.

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

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