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 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 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 Effectiveness and Prognosis: Drainage Skin-Bridge Sparing Surgery Combined with Fistulotomy versus Fistulotomy Only in the Treatment of Anal Fistula

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiao Wang ◽  
Chunliang Wang ◽  
Ruihua Qi
Keyword(s):  
Anal Fistula ◽  
Postoperative Bleeding ◽  
Length Of Hospital Stay ◽  
Control Group ◽  
Anal Function ◽  
Double Blind ◽  
Voiding Function ◽  
Vas Scores ◽  
The Difference ◽  
Better Than

Objective. This study intends to analyze the difference in the efficacy of drainage skin-bridge sparing surgery combined fistulotomy (DSCF) and fistulotomy alone. Methods. 125 patients with anal fistula were enrolled as study subjects and randomly divided into control group (CG) and observation group (OG) by double-blind lottery. The CG received drainage skin-bridge sparing surgery with fistulotomy and the OG received fistulotomy only. Results. The VAS scores of the trauma in the OG were lower than those in the CG on 1st day of surgery and 7 days after surgery ( P < 0.05 ). The length of hospital stay and time to wound healing were shorter in the OG than in the CG ( P < 0.05 ). The incidence of postoperative bleeding in the OG was 9.52%, which was lower than 22.58% in the CG ( P < 0.05 ). The rectal examination scores were lower in the OG than in the CG at 3 and 5 days postoperatively ( P < 0.05 ). The Wexner scores of solid incontinence (0 to 4), liquid incontinence (0 to 4), gas incontinence (0 to 4), pad wearing (0 to 4), and lifestyle alteration (0 to 4) in the OG were lower than those of the CG at 5 days postoperatively ( P < 0.05 ). Voiding function scores were lower in the OG than in the CG at 2 and 3 days postoperatively ( P < 0.05 ). Conclusions. The efficacy of drainage skin-bridge sparing surgery combined fistulotomy is better than that of fistulotomy alone, which can accelerate postoperative healing, enhance urinary function, reduce postoperative bleeding, and improve anal function.

scholarly journals PENGARUH MENDENGARKAN MUROTTAL AL-QUR’AN TERHADAP MEMORI KERJA

2019 ◽  
Vol 5 (1) ◽  
pp. 24-32
Author(s):  
Istiqomah Nur Aziza ◽  
Nanang Wiyono ◽  
Afia Fitriani
Keyword(s):  
Working Memory ◽  
Span Task ◽  
Control Group ◽  
Research Subjects ◽  
Operation Span ◽  
Significant Difference ◽  
Post Test ◽  
The Mind ◽  
Experimental Group ◽  
Test Control

It is important to optimize working memory because it transforms, synergizes and constantly updates new and old information. One way to optimize working memory is to listen to Murottal Al-Qur'an, because it has a harmonious tone which can stabilize the mind to properly process the information. This study aims to determine the effect of listening to the Al-Quran murottal on working memory. The research subjects were 24 students of Psikologi 2017, grouped equally in the control and experimental groups. An experimental intervention was administered for 15 consecutive days lasting 15 minutes and 52 seconds. Measurements in working memory use Operation Span Task, Reading Span Task and Symmetry Span Task. The design of the study used a pre-test post-test control group and the data were analyzed by t-test. The results showed a significant difference between the control group and the experimental group on the symmetry span task subtest (p = 0.044, p <0.05).

scholarly journals DAMPAK PELAKSANAAN MODEL MATEMATIKA REALISTIS PADA PENINGKATAN KEMAMPUAN PENALARAN DEDUKTIF SISWA SEKOLAH MENENGAH DI PARUNG KABUPATEN - KABUPATEN BOGOR

2010 ◽  
Vol 11 (1) ◽  
pp. 10-19
Author(s):  
Dr. Yumiati M.Si. ◽  
Tarhadi
Keyword(s):  
Deductive Reasoning ◽  
Learning Ability ◽  
Control Group ◽  
Realistic Mathematics Education ◽  
Research Subjects ◽  
Conventional Teaching ◽  
Learning Mathematics ◽  
Teachers And Students ◽  
Experimental Group ◽  
Perceptions Of Teachers

This study attemps to determine impact of the implementation of Realistic Mathematics Education (RME) model on the improvement of deductive reasoning ability, and to describe the perceptions of teachers and students of secondary schools toward the RME model. A quasi-experiment with pretest-postest non-equivalent group as research design was employed into 2 groups of research subject: the experimental group and the control group and each was treated under RME and conventional teaching model. A number of 44 students of SMPN1 Parung and 20 students of SMP Islam Terpadu Jabon Mekar in Parung District were chosen as research subjects. Quantitative data obtained through questionnaires and written tests were analyzed using Student-t test. While qualitative information gathered through responses to personal interview and observations was analyzed using qualitative-naturalistics method. Findings reveals that learning mathematics using the RME model impacts on increasing students deductive learning ability in both schools with amount of increase is 30.7 for SMP Islam Terpadu and 44.8 for SMPN1 Parung. RME model also impacts on developing students positive attitude of students toward mathematics as seen in the number of students who liked mathematics lesson, that is 100% for SMP Islam Terpadu and 97% SMPN 1 Parung. The perceptions of teachers and students in both schools toward the RME Model are positive. Application of learning mathematics using PMR model becomes more enjoyable and students become more active.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

scholarly journals Late pregnancy analysis with Yunban’s remote fetal monitoring system

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983283
Author(s):  
Qiuping Wang ◽  
Weihua Yang ◽  
Lie Li ◽  
Guokai Yan ◽  
Huihui Wang ◽  
...  
Keyword(s):  
Late Stage ◽  
Stress Test ◽  
Fetal Monitoring ◽  
Late Pregnancy ◽  
Cardiac Monitoring ◽  
Control Group ◽  
Research Subjects ◽  
Neonatal Asphyxia ◽  
The Difference ◽  
Experimental Group

With the adoption of the two-child policy, there has been a large increase in women of older maternal and high-risk pregnant women. So, it is necessary to analyze the health status of women in the late pregnancy on time. To analyze the effect on using remote fetal monitoring on women in the late pregnancy, we selected women in the late stage of pregnancy in our hospital as research subjects. They were randomly divided into two groups: the experimental group, which engaged in remote fetal monitoring, and the control group, which adopted traditional cardiac monitoring. In order to get more effective data, we used the Kalman filter and audio repair algorithms to preprocess the collected data. During follow-up observation, we compared the two groups using neonatal cardiac monitoring by employing the non-stress test and observed the occurrence of neonatal asphyxia. The incidence of neonatal abnormal non-stress test in the experimental group and the control group was 33.6% and 17.3%, respectively; the difference was statistically significant ( p < 0.05). The incidence of neonatal asphyxia in the experimental group was 12.5%, which was significantly lower than in the control group (30%; p < 0.05). We have found that women in the late stage of pregnancy who adopted remote fetal monitoring could detect abnormal non-stress test earlier and thus increase in the detection of rate of neonatal asphyxia.

scholarly journals The Use of Augmented Reality Blended Learning for Improving Understanding of Food Security

2020 ◽  
Vol 9 (1) ◽  
pp. 59-69
Author(s):  
H. Pujiastuti ◽  
R. Haryadi
Keyword(s):  
Food Security ◽  
Augmented Reality ◽  
Blended Learning ◽  
Experimental Method ◽  
Learning System ◽  
Control Group ◽  
Learning Approach ◽  
Research Subjects ◽  
Experimental Group

The purpose of this study was to increase students’ understanding of the food security concept. The experimental method was being applied in this research. Moreover, research subjects were divided into two groups, one as an experimental group and another as a control group. The subjects of this study were 100 students at the Universitas Sultan Ageng Tirtayasa. Fifty students are guided under Augmented Reality based blended learning system as the experimental group. Another class of 50 students is on the control group, which studies with the conventional blended learning approach. The experimental class obtained 73% of the N-gain result, while control class obtained 50%. Therefore, it can be concluded that using augmented reality can improve the students’ understanding of the food security concept.

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