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 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 Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

scholarly journals Back Propagation Neural Network-Based Ultrasound Image for Diagnosis of Cartilage Lesions in Knee Osteoarthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoming Zhao ◽  
Wei Gong ◽  
Xing Li ◽  
Weibing Yang ◽  
Dengfeng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Ultrasound Image ◽  
Kappa Coefficient ◽  
Control Group ◽  
Ultrasound Images ◽  
The Difference ◽  
Experimental Group

Objective. To explore the application value of ultrasound image based on back propagation (BP) neural network algorithm in knee osteoarthritis (KOA) and evaluate the application effect and value of ultrasound image technology based on the BP neural network in the diagnosis of knee osteoarthritis cartilage lesions, 98 patients who were admitted to our hospital were diagnosed with KOA and had undergone arthroscopic soft tissue examinations were randomly selected. According to whether image processing was performed, the ultrasound images of all patients were divided into two groups. The control group was image before processing, and the experimental group was image after processing optimization. The consistency of the inspection results of the ultrasound images before and after the processing with the arthroscopy results was compared. The results showed that the staging accuracy of the control group was 68.3% and that of the experimental group was 76.9%. The accuracy of staging cartilage degeneration of the experimental group was higher than that of the control group, and the difference was not remarkable ( P > 0.05 ). The kappa coefficient of the experimental group was 0.61, and that of the control group was 0.40. The kappa coefficient of the experimental group was higher than that of the control group, and the difference was significant ( P < 0.05 ). Conclusion. The inspection effect of the ultrasound image processed by the BP neural network was superior to that of the conventional ultrasound image. It reflected the good adoption prospect of neural networks in image processing.

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.

An Investigation of the Effects of Curcumin on the Changes in the Central Nervous System of Rats Exposed to Aroclor 1254 in the Prenatal Period

2018 ◽  
Vol 17 (2) ◽  
pp. 132-143 ◽  
Author(s):  
Mehmet Eray Alcigir ◽  
Halef Okan Dogan ◽  
Begum Yurdakok Dikmen ◽  
Kubra Dogan ◽  
Sevil Atalay Vural ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Neuron Specific Enolase ◽  
Control Group ◽  
Albino Rats ◽  
Prenatal Period ◽  
Aroclor 1254 ◽  
Rat Pups ◽  
Tunel Reaction ◽  
The Central Nervous System

Background & Objective: Aroclor 1254 is a widespread toxic compound of Polychlorinated Biphenyls (PCBs), which can create significant nervous problems. No remedies have been found to date. The aim of this study was to reveal the damage that occurs in the central nervous system of rat pups exposed to Aroclor 1254 in the prenatal period and to show the inhibiting effect of curcumin, which is a strong anti-oxidant and neuroprotective substance. Method: The study established 3 groups of adult female and male Wistar albino rats. The rats were mated within these groups and the offspring rats were evaluated within the group given Aroclor 1254 only (n=10) and the group was given both Aroclor 1254 and curcumin (n=10) and the control group (n=10). The groups were compared in respect of pathomorphological damage. The immunohistochemical evaluation was made of 8-hydroxdeoxyguanosine (8-OHdG), 4-hydroxynoneal (4HNE), myelin basic protein (MBP) expressions and TUNEL reaction. The biochemical evaluation was made of the changes in the TAS-TOS and Neuron Specific Enolase (NSE) levels. Damage was seen to have been reduced with curcumin in the 8OHdG and TUNEL reactions, especially in the forebrain and the midbrain, although the dosage applied did not significantly change TAS and TOS levels. Consequently, it was understood that Aroclor 1254 caused damage in the central nervous system of the pup in the prenatal period, and curcumin reduced these negative effects, particularly in the forebrain and the midbrain. Conclusion: It was concluded that curcumin could be a potential neuroprotective agent and would be more effective at higher doses.

scholarly journals Multiplexed neurochemical transmission emulated using a dual-excitatory synaptic transistor

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mingxue Ma ◽  
Yao Ni ◽  
Zirong Chi ◽  
Wanqing Meng ◽  
Haiyang Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Long Term Potentiation ◽  
Short Term ◽  
Term Potentiation ◽  
Binary Neural Network ◽  
Neuromorphic Systems ◽  
Human Brains

AbstractThe ability to emulate multiplexed neurochemical transmission is an important step toward mimicking complex brain activities. Glutamate and dopamine are neurotransmitters that regulate thinking and impulse signals independently or synergistically. However, emulation of such simultaneous neurotransmission is still challenging. Here we report design and fabrication of synaptic transistor that emulates multiplexed neurochemical transmission of glutamate and dopamine. The device can perform glutamate-induced long-term potentiation, dopamine-induced short-term potentiation, or co-release-induced depression under particular stimulus patterns. More importantly, a balanced ternary system that uses our ambipolar synaptic device backtrack input ‘true’, ‘false’ and ‘unknown’ logic signals; this process is more similar to the information processing in human brains than a traditional binary neural network. This work provides new insight for neuromorphic systems to establish new principles to reproduce the complexity of a mammalian central nervous system from simple basic units.

scholarly journals Effects of Electroacupuncture on Pain Threshold of Laboring Rats and the Expression of Norepinephrine Transporter andα2 Adrenergic Receptor in the Central Nervous System

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Qianli Tang ◽  
Qiuyan Jiang ◽  
Suren R. Sooranna ◽  
Shike Lin ◽  
Yuanyuan Feng ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Adrenergic Receptor ◽  
Pain Threshold ◽  
Norepinephrine Transporter ◽  
Control Group ◽  
Tail Flick ◽  
Pregnant Rats ◽  
Tail Flick Test ◽  
The Central Nervous System

To observe the effects of electroacupuncture on pain threshold of laboring rats and the expression of norepinephrine transporter andα2 adrenergic receptor in the central nervous system to determine the mechanism of the analgesic effect of labor. 120 pregnant rats were divided into 6 groups: a control group, 4 electroacupuncture groups, and a meperidine group. After interventions, the warm water tail-flick test was used to observe pain threshold. NE levels in serum, NET, andα2AR mRNA and protein expression levels in the central nervous system were measured. No difference in pain threshold was observed between the 6 groups before intervention. After intervention, increased pain thresholds were observed in all groups except the control group with a higher threshold seen in the electroacupuncture groups. Serum NE levels decreased in the electroacupuncture and MP groups. Increases in NET andα2AR expression in the cerebral cortex and decreases in enlarged segments of the spinal cord were seen. Acupuncture increases uptake of NE via cerebral NET and decreases its uptake by spinal NET. The levels ofα2AR are also increased and decreased, respectively, in both tissues. This results in a decrease in systemic NE levels and may be the mechanism for its analgesic effects.

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