Monitoring of Neuroendocrine Changes in Acute Stage of Severe Craniocerebral Injury by Transcranial Doppler Ultrasound Image Features Based on Artificial Intelligence Algorithm

This study was aimed at exploring the application value of transcranial Doppler (TCD) based on artificial intelligence algorithm in monitoring the neuroendocrine changes in patients with severe head injury in the acute phase; 80 patients with severe brain injury were included in this study as the study subjects, and they were randomly divided into the control group (conventional TCD) and the experimental group (algorithm-optimized TCD), 40 patients in each group. An artificial intelligence neighborhood segmentation algorithm for TCD images was designed to comprehensively evaluate the application value of this algorithm by measuring the TCD image area segmentation error and running time of this algorithm. In addition, the Glasgow coma scale (GCS) and each neuroendocrine hormone level were used to assess the neuroendocrine status of the patients. The results showed that the running time of the artificial intelligence neighborhood segmentation algorithm for TCD was 3.14 ± 1.02 s , which was significantly shorter than 32.23 ± 9.56 s of traditional convolutional neural network (CNN) algorithms ( P < 0.05 ). The false rejection rate (FRR) of TCD image area segmentation of this algorithm was significantly reduced, and the false acceptance rate (FAR) and true acceptance rate (TAR) were significantly increased ( P < 0.05 ). The consistent rate of the GCS score and Doppler ultrasound imaging diagnosis results in the experimental group was 93.8%, which was significantly higher than the 80.3% in the control group ( P < 0.05 ). The consistency rate of Doppler ultrasound imaging diagnosis results of patients in the experimental group with abnormal levels of follicle stimulating hormone (FSH), prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) was significantly higher than that of the control group ( P < 0.05 ). In summary, the artificial intelligence neighborhood segmentation algorithm can significantly shorten the processing time of the TCD image and reduce the segmentation error of the image area, which significantly improves the monitoring level of TCD for patients with severe craniocerebral injury and has good clinical application value.

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Imaging Features of Artificial Intelligence Algorithm in the Analysis of Cerebral Protective Effect of Craniotomy Hematoma Removal under Propofol Anesthesia in Patients with Chronic Subdural Hematoma

Scientific Programming ◽

10.1155/2021/6435476 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Manyun Bai ◽

Yufang Li ◽

Qian Zhao ◽

Renzhong Guo

Keyword(s):

Artificial Intelligence ◽

Protective Effect ◽

Subdural Hematoma ◽

Chronic Subdural Hematoma ◽

Ct Images ◽

Control Group ◽

Brain Ct ◽

Intelligence Algorithm ◽

Artificial Intelligence Algorithm ◽

Experimental Group

Objective. The aim of this work was to study the cerebral protective effect of craniotomy hematoma removal under propofol anesthesia based on the artificial intelligence algorithm analysis of the changes in imaging characteristics of chronic subdural hematoma (CSDH) patients. Methods. A total of 60 CSDH patients who were treated in hospital were recruited and were randomly rolled into an experimental group and a control group, with 30 people in each group. Patients in the experimental group were treated with propofol anesthesia + craniotomy hematoma removal, while those in the control group were treated with conventional anesthesia + craniotomy hematoma removal. Head CT examinations were performed on the next day, one week, one month, three months, and six months after the operation. A two-dimensional empirical mode decomposition (BEMD) algorithm was used for edge detection and denoising of brain CT images of CSDH patients. Then, the amount of hematoma was calculated, and the Markwalder grading was performed to evaluate the neurological function. The number of recurrence and reoperation cases within six months of follow-up was collected. Results. 1. The quality of CT images was remarkably improved after processing with artificial intelligence algorithms. 2. The amount of hematoma in the experimental group was remarkably lower than that in the control group at January, March, and June after surgery (12.89 ± 2.12 VS 20.32 ± 16.41; 7.55 ± 4.13 VS 15.88 ± 14.22; 3.39 ± 3.79 VS 6.55 ± 3.69, P < 0.05 ). 3. The experimental group was remarkably better than the control group in Markwalder grading three months and six months after the operation ( P < 0.05 ). Conclusion. Artificial intelligence algorithm had good effect on the brain CT image processing of CSDH patients, and craniotomy hematoma removal under propofol anesthesia had an ideal brain protection effect.

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Colored Doppler Ultrasound Diagnosis and Pathological Analysis of Superficial Organ and Vascular Diseases

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3333 ◽

2021 ◽

Vol 11 (3) ◽

pp. 903-911

Author(s):

Wenting Jiang ◽

Duxing Xu ◽

Xiaofeng Zhang ◽

Mingyuan Wu ◽

Kunbin Wu

Keyword(s):

Doppler Ultrasound ◽

Ultrasound Imaging ◽

Compliance Rate ◽

Lower Extremities ◽

Pathological Examination ◽

Control Group ◽

Imaging Technology ◽

Doppler Ultrasound Imaging ◽

Experimental Group ◽

Superficial Tissue

This paper proposes a fully digital signal processing scheme for ultrasonic Doppler endoscope imaging. 200 patients with superficial tissue lumps were randomly divided into two groups: the control group and the experimental group. These two groups used conventional ultrasound examination and colored Doppler ultrasound imaging technology, respectively, to observe and compare the test method and the surgical pathological examination results. Compared with the results of the two groups, the diagnostic compliance rate of the patients in the experimental group was 99.0% significantly higher than the diagnostic compliance rate of 86.0%. At the same time, 300 patients with surgery and pathologically confirmed superficial organ lesions were selected in the hospital, and all patients were diagnosed by ultrasound to observe the diagnosis. The clinical effects of colored Doppler ultrasound to diagnose vascular lesions in the lower extremities of diabetes were discussed. The rate of arteriosclerosis in the lower extremities of the observation group was 92.32%. The more than 50% vascular stenosis rate was 45.16%. The vascular blocking rate was 16. 13% and thrombosis rate 6.45% were significantly higher than the control group 12.90%, 8.06%, 0.00%, 0.00%. In the diagnosis of superficial tissue lesions, the resolution of colored Doppler ultrasound imaging technology is relatively high, which can significantly improve the clinical diagnosis rate and has clinical application value.

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Improvement of Pre-Service Teachers' Practical Knowledge and Motivation about Artificial Intelligence through a Service-learning-based Module in Guizhou, China: A Quasi-Experimental Study

Asian Journal of University Education ◽

10.24191/ajue.v17i3.14499 ◽

2021 ◽

Vol 17 (3) ◽

pp. 203

Author(s):

Song Pu ◽

Nor Aniza Ahmad ◽

Mas Nida Md. Khambari ◽

Ng Keng Yap ◽

Seyedali Ahrari

Keyword(s):

Artificial Intelligence ◽

Service Learning ◽

Primary School ◽

Practical Knowledge ◽

Control Group ◽

Primary School Students ◽

School Students ◽

Control Group Design ◽

Quasi Experimental ◽

Experimental Group

Abstract. The purpose of this study is twofold: 1) to develop a service-learning-based module training artificial intelligence (AI) subject (SLBM-TAIS), and 2) to evaluate the effect of SLBM-TAIS on pre-service teachers’ (PSTs’) practical knowledge and motivation, as well as primary school students' attitude towards AI in China. Participants of this study comprised 60 PSTs and 107 primary school students. The experimental research in this study followed the quasi-experimental non-randomized pre-test and post-test control group design. The PSTs were divided into experimental and control groups, and the primary school students followed the same grouping. The PSTs in the experimental group taught AI subjects to the primary school students in the experimental group, while the PSTs in the control group taught AI subjects to the primary school students in the control group. The results of the study showed that SLBM-TAIS was effective in training PSTs to teach AI subjects to primary school students. Furthermore, the SLBM-TAIS developed in this study offered a unique technique for training PSTs and primary school students that could increase PSTs' practical knowledge and motivation, as well as primary school students' attitudes toward AI. The findings from this study are important in the field of educational psychology, and its contribution has several theoretical and practical implications. Keywords: Attitude; artificial intelligence; pre-service teachers; primary school students; practical knowledge; motivation

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The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software

BDJ ◽

10.1038/s41415-021-3526-6 ◽

2021 ◽

Vol 231 (8) ◽

pp. 481-485

Author(s):

Hugh Devlin ◽

Tomos Williams ◽

Jim Graham ◽

Martin Ashley

Keyword(s):

Artificial Intelligence ◽

Control Group ◽

Preventive Dentistry ◽

Proximal Caries ◽

Non Invasive ◽

Assessment Study ◽

Carious Lesions ◽

Dental Practitioners ◽

Caries Assessment ◽

Experimental Group

AbstractIntroduction Reversal of enamel-only proximal caries by non-invasive treatments is important in preventive dentistry. However, detecting such caries using bitewing radiography is difficult and the subtle patterns are often missed by dental practitioners.Aims To investigate whether the ability of dentists to detect enamel-only proximal caries is enhanced by the use of AssistDent artificial intelligence (AI) software.Materials and methods In the ADEPT (AssistDent Enamel-only Proximal caries assessmenT) study, 23 dentists were randomly divided into a control arm, without AI assistance, and an experimental arm, in which AI assistance provided on-screen prompts indicating potential enamel-only proximal caries. All participants analysed a set of 24 bitewings in which an expert panel had previously identified 65 enamel-only carious lesions and 241 healthy proximal surfaces.Results The control group found 44.3% of the caries, whereas the experimental group found 75.8%. The experimental group incorrectly identified caries in 14.6% of the healthy surfaces compared to 3.7% in the control group. The increase in sensitivity of 71% and decrease in specificity of 11% are statistically significant (p <0.01).Conclusions AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries and could be considered as a tool to support preventive dentistry in general practice.

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Diagnostic Value of Color Doppler Ultrasound Combined with Superb Microvascular Imaging in the Detection of Small Renal Tumors Less than 3 cm Treated with Jinkui Shenqi Pills

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5327331 ◽

2021 ◽

Vol 2021 ◽

pp. 1-5

Author(s):

Yuping Gong ◽

Shuhui Li

Keyword(s):

Doppler Ultrasound ◽

Color Doppler ◽

Statistical Significance ◽

Color Doppler Ultrasound ◽

Diagnostic Value ◽

Renal Tumors ◽

Control Group ◽

Detection Accuracy ◽

Experimental Group ◽

Negative Detection

The purpose of this study was to investigate the diagnostic value of color Doppler ultrasound combined with superb microvascular imaging (SMI) in the detection of small renal tumors less than 3 cm treated with Jinkui Shenqi pills. 50 cases were randomly selected from the patients with angioleiomyoma (a kind of small renal tumor) less than 3 cm confirmed by pathological examination and treated in our hospital from January 2018 to January 2020. All patients were treated with Jinkui Shenqi pills. All patients were first detected by color Doppler ultrasound and then by SMI. The results of color Doppler ultrasound were used as the control group, while those of color Doppler ultrasound combined with SMI were used as the experimental group. After that, the specificity, sensitivity, positive and negative detection results, and detection accuracy were compared between the two groups. The specificity and sensitivity in the experimental group were significantly higher than those in the control group, with statistical significance ( P < 0.05 ). The cases of positive and negative detection results in the experimental group were significantly higher than those in the control group, with statistical significance ( P < 0.05 ). The detection accuracy in the experimental group was significantly higher than that in the control group, with statistical significance ( P < 0.05 ). The specificity, sensitivity, positive and negative detection results, and detection accuracy of color Doppler ultrasound combined with SMI in the detection of small renal tumors less than 3 cm treated with Jinkui Shenqi pills were all significantly higher than those of color Doppler ultrasound; therefore, the application of color Doppler ultrasound combined with SMI for the diagnosis of small renal tumors is of high value.

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

Journal of Healthcare Engineering ◽

10.1155/2021/2603842 ◽

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.

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Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease

Contrast Media & Molecular Imaging ◽

10.1155/2021/6423398 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Lingzhi Hong ◽

Xufang Cheng ◽

Deming Zheng

Keyword(s):

Quality Of Life ◽

Artificial Intelligence ◽

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Controlled Trial ◽

Control Group ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Experimental Group

The research achievements of artificial intelligence technology in the development of chronic obstructive pulmonary disease were explored, and the advantages and problems encountered in the development of intelligent nursing were analyzed. This paper presents the application of artificial intelligence in the emergency care of patients with chronic obstructive pulmonary disease. The method included 447 COPD patients in a randomized controlled trial to observe the improvement of quality of life at 4 and 12 months after artificial intelligence medical intervention. A prospective randomized controlled trial included 101 patients with COPD who underwent a 9-month web-based knowledge exercise on the prevention of acute exacerbation of COPD through artificial intelligence medicine and were randomly divided into two groups: the experimental group and the control group. The results show that, in the experimental group and the control group, after 4 months, the quality of life does not change; after 12 months, compared with controls, the quality of life and emotional and psychological conditions have improved obviously. 29 patients who participated in the experiment and were randomly divided into the experimental group and the control group showed satisfactory results. COPD hospitalized rate and length of hospital stay were decreased in the experimental group than in the control group. For single-factor analysis, artificial intelligence medical intervention has not achieved significant significance, and the experimental results have preliminarily confirmed the effectiveness of artificial intelligence medical treatment.

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Artificial Intelligence Algorithm-Based Lumbar and Spinal MRI for Evaluation of Efficacy of Chinkuei Shin Chewan Decoction on Lumbar Spinal Stenosis

Contrast Media & Molecular Imaging ◽

10.1155/2021/2700452 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yuefeng Zhu ◽

Tao Wu ◽

Wenhao Wang ◽

Chengchen Cai ◽

Bin Zhu ◽

...

Keyword(s):

Artificial Intelligence ◽

Spinal Stenosis ◽

Lumbar Spinal Stenosis ◽

Control Group ◽

Research Subjects ◽

Registration Algorithm ◽

Lumbar Mri ◽

Magnetic Resonance Imaging Mri ◽

Lumbar Spinal ◽

Experimental Group

The study aimed to explore the application value of lumbar Magnetic Resonance Imaging (MRI) images processed by artificial intelligence algorithms in evaluating the efficacy of chinkuei shin chewan decoction (a traditional Chinese medicine to nourish the kidney) in the treatment of lumbar spinal stenosis (LSS). Specifically, 110 LSS patients admitted to the hospital were selected as the research subjects. They were randomly divided into the control group (n = 55) and experimental group (n = 55) according to different treatment methods. The control group was treated with traditional medicine, and the experimental group additionally took chinkuei shin chewan decoction on its basis. Based on the traditional U-net algorithm, a U-net registration algorithm based on artificial intelligence was designed by introducing the information entropy theory, and the algorithm was applied to the lumbar MRI image evaluation of LSS patients. Compared with the traditional U-net algorithm, the artificial intelligence-based U-net registration algorithm had a decreased noise level P < 0.05 , the Jaccard (J) value (0.84) and the Dice value (0.93) increased significantly versus the traditional algorithm (J = 0.63, Dice = 0.81), and the characteristics of the image were more accurate. Before treatment, the Oswestry Disability Index (ODI) scores of the experimental group and the control group were 44.32 ± 6.45 and 43.32 ± 5.45, respectively. After treatment, the ODI scores of the two groups were 10.21 ± 5.05 and 17.09 ± 5.23, respectively. Both showed significant improvement, while the improvement of the experimental group was more obvious than that of the control group P < 0.05 . The overall effective rates of the two groups of patients were 96.44% and 82.47%, respectively, and the experimental group was significantly higher than the control group P < 0.05 . Under the U-net registration algorithm based on artificial intelligence, the diagnostic accuracy of lumbar MRI in the experimental group was 94.45%, significantly higher than 67.5% before the introduction of the algorithm P < 0.05 . In conclusion, chinkuei shin chewan decoction are effective for the treatment of LSS, and lumbar MRI based on the artificial intelligence U-net registration algorithm can evaluate the efficacy of LSS well and is worthy of promotion.

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Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/9581568 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Na Ma ◽

Xiujie Wang ◽

Xinxin Zhao ◽

Xuehan Zhao ◽

Lin Liu

Keyword(s):

Endoscopic Resection ◽

Marked Point ◽

Ultrasound Image ◽

Nursing Intervention ◽

Image Features ◽

Segmentation Algorithm ◽

Decomposition Algorithm ◽

Control Group ◽

Colon Polyps ◽

Experimental Group

Based on the ultrasonic imaging and endoscopic resection of the intelligent segmentation algorithm, this study is aimed at exploring whether nursing intervention can promote the good recovery of patients with colon polyps, hoping to find a new method for clinical treatment of the colon polyps. Patients with colon polyps were divided into an experimental group (fine nursing) and a control group (general nursing). The colonoscopy polyp ultrasound image was preprocessing to select the seed points and background points. The random walk decomposition algorithm was applied to calculate the probability of each marked point, and then, the marked image was outputted. The accuracy of the intelligent segmentation algorithm was 81%. The incidence of complications in the experimental group was 4.83%, which was lower than 16.66% in the control group, and the difference was statistically obvious ( P < 0.05 ). Perioperative refined nursing intervention for colon polyp patients undergoing endoscopic electrosurgical resection can decrease postoperative adverse reactions; reduce postoperative mucosal perforation, blood in the stool, abdominal pain, and small bleeding; lower the incidence of postoperative complications; and allow patients to recover quickly, enhancing the life comfort of patient.

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Artificial Intelligence Chatbot for Anxiety and Depression for University Students: a Pilot Randomized Controlled Trial (Preprint)

10.2196/preprints.20678 ◽

2020 ◽

Author(s):

Maria Carolina Klos ◽

Milagros Escoredo ◽

Angie Joerin ◽

Viviana Noemí Lemos ◽

Michiel Rauws ◽

...

Keyword(s):

Artificial Intelligence ◽

College Students ◽

Randomized Controlled Trial ◽

Depressive Symptoms ◽

Controlled Trial ◽

Anxiety Symptoms ◽

Control Group ◽

Depression And Anxiety ◽

Randomized Controlled ◽

Experimental Group

BACKGROUND The use of artificial intelligence based chatbots as an instrument of psychological intervention is emerging, however no studies have been reported in Latin America. OBJECTIVE This study aims to evaluate usage patterns and whether the use of a chatbot is effective for relieving depression and anxiety symptoms compared to a control group utilizing a psychoeducation book in Argentina. METHODS This was a randomized controlled trial study utilizing the chabot Tess throughout eight weeks. The initial sample consisted of 181 Argentinian college students ages 18 to 33, 87.2% female. Of those, 33 participants in the experimental condition and 30 in the control condition provided data on depressive symptoms at week eight, and 27 participants in the experimental condition and 23 in the control condition provided data on anxiety symptoms at week eight. Between and within group comparisons were analysed using Mann-Whitney U and Wilcoxon tests for depression symptoms, and Independent and Paired Samples t Tests to analyze anxiety symptoms. RESULTS There was no significant intergroup differences between the experimental group and the control group for depression and anxiety symptoms from baseline to week eight (P>.05). However, there were significant intragroup differences, where the experimental group showed a significant decrease in anxiety symptoms (P=.04) and no differences were observed for the control group (P=.33). No significant differences were found for depressive symptoms within the groups (P>.05). The effect size of the intervention was moderate for anxiety (d=.50) and small for depression (r=.09). In regards to participants engagement after eight weeks, there was an average of 472 exchanged messages (M=472.15; SD=249.52) and a higher number of messages exchanged with Tess was associated with positive feedback (F2,36=4.37; P=.02). CONCLUSIONS Students engaged a considerable amount of time exchanging messages with Tess and positive feedback was associated with higher numbers of messages exchanged. The initial results show promising evidence for the use of Tess for anxiety symptoms and a lower effect on depressive symptoms in Argentinian college students. Research on chatbots is still in its initial stages and further research is needed.

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