Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT

The objective is to study the application of spiral CT in the diagnosis of the trachea in children. In this study, the effect of 64-slice multislice spiral CT in diagnosing infant bronchial bridge was studied based on an artificial neural network. From June 2020 to December 2020, 100 cases of children with the trachea in the outpatient department of our hospital were selected as the research object. They were divided into the study group and the control group, with 50 cases in each group. The results showed that among the 50 cases in the control group, 42 cases were found to have a bronchial foreign body and 8 cases were missed; the detection rate was 84%. There were 7 cases of trachea foreign body, 15 cases of left bronchial foreign body, 14 cases of right bronchial foreign body, and 6 cases of medium bronchial foreign body. The detection rate of the study group was significantly higher than that of the control group, with a statistical significance ( P < 0.05 ). Conclusion. The detection rate of neural networks in MSCT is higher than that of X-ray, and the MSCT based on the artificial neural network can clearly show the morphology, position, and the relationship between the foreign body and trachea, which is worthy of clinical promotion and application.

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Automation of systematic reviews through artificial neural network modeling and machine learning: A methodological protocol report (Preprint)

10.2196/preprints.26448 ◽

2020 ◽

Author(s):

Gabriel Ferraz Ferreira Sr ◽

Marcos Gonçalves Quiles Sr ◽

Tiago Santana Nazare Sr ◽

Solange Oliveira Rezende ◽

Marcelo Demarzo Sr

Keyword(s):

Neural Network ◽

Machine Learning ◽

Systematic Review ◽

Artificial Neural Network ◽

Systematic Reviews ◽

Selection Process ◽

Automated System ◽

Neural Network Modeling ◽

Control Group ◽

Artificial Neural

UNSTRUCTURED Background: A systematic review can be defined as a summary of the evidence found in the literature via a systematic search in the available scientific databases. One of the steps involved is article selection, which is typically a laborious task. Machine learning and artificial intelligence can be important tools in automating this step, thus aiding researchers. The aim of this study is to create models based on an artificial neural network system and machine learning to automate the article selection process in systematic reviews in the area of Mindfulness. Methods: The study will be performed using R programming software. The system will consist of six main steps: 1) data import; 2) exclusion of duplicates; 3) exclusion of nonarticles; 4) article reading and model creation using artificial neural networks; 5) comparison of the models; and 6) system sharing. We will choose the 10 most relevant systematic reviews published in the fields of “Mindfulness and Health Promotion” and “Orthopedics and Traumatology” (control group) to serve as a test of the effectiveness of the article selection. The final results for these two fields will be compared. Conclusion: An automated system with a modifiable sensitivity will be created to select scientific articles in systematic review that can be expanded to various fields. We will disseminate our results and models through the “Observatory of Evidence” in public health, an open and online platform that will assist researchers in systematic reviews.

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Improving the Accuracy of an Artificial Neural Network Using Multiple Differently Trained Networks

Neural Computation ◽

10.1162/neco.1992.4.5.772 ◽

1992 ◽

Vol 4 (5) ◽

pp. 772-780 ◽

Cited By ~ 66

Author(s):

William G. Baxt

Keyword(s):

Neural Network ◽

Myocardial Infarction ◽

Artificial Neural Network ◽

False Alarm ◽

False Alarm Rate ◽

Detection Rate ◽

Rate Sensitivity ◽

The Other ◽

Risk Patients ◽

Artificial Neural

When either detection rate (sensitivity) or false alarm rate (specificity) is optimized in an artificial neural network trained to identify myocardial infarction, the increase in the accuracy of one is always done at the expense of the accuracy of the other. To overcome this loss, two networks that were separately trained on populations of patients with different likelihoods of myocardial infarction were used in concert. One network was trained on clinical pattern sets derived from patients who had a low likelihood of myocardial infarction, while the other was trained on pattern sets derived from patients with a high likelihood of myocardial infarction. Unknown patterns were analyzed by both networks. If the output generated by the network trained on the low risk patients was below an empirically set threshold, this output was chosen as the diagnostic output. If the output was above that threshold, the output of the network trained on the high risk patients was used as the diagnostic output. The dual network correctly identified 39 of the 40 patients who had sustained a myocardial infarction and 301 of 306 patients who did not have a myocardial infarction for a detection rate (sensitivity) and false alarm rate (1-specificity) of 97.50 and 1.63%, respectively. A parallel control experiment using a single network but identical training information correctly identified 39 of 40 patients who had sustained a myocardial infarction and 287 of 306 patients who had not sustained a myocardial infarction (p = 0.003).

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The new SUMPOT to predict postoperative complications using an Artificial Neural Network

Scientific Reports ◽

10.1038/s41598-021-01913-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Cosimo Chelazzi ◽

Gianluca Villa ◽

Andrea Manno ◽

Viola Ranfagni ◽

Eleonora Gemmi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Postoperative Complications ◽

Classification Accuracy ◽

Surgical Patients ◽

Control Group ◽

Emergency Procedures ◽

High Dependency ◽

Artificial Neural ◽

Testing Set

AbstractAn accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality in high-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial Neural Network technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohort of 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units, high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, and a testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of the accuracy in detecting those patients who will develop postoperative complications. A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperative complications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classification accuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51 were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified). The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications, suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirm its applicability in routine clinical practice.

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The New SUMPOT to Predict Postoperative Complications Using an Artificial Neural Network

10.21203/rs.3.rs-728023/v1 ◽

2021 ◽

Author(s):

Cosimo Chelazzi ◽

Gianluca Villa ◽

Andrea Manno ◽

Viola Ranfagni ◽

Eleonora Gemmi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Postoperative Complications ◽

Scoring Systems ◽

Surgical Patients ◽

Control Group ◽

Emergency Procedures ◽

High Dependency ◽

Artificial Neural ◽

Testing Set

Abstract An accurate assessment of preoperative risk may improve use of hospital resources and reduce morbidity and mortality inhigh-risk surgical patients. This study aims at implementing an automated surgical risk calculator based on Artificial NeuralNetwork technology to identify patients at risk for postoperative complications. We developed the new SUMPOT based on risk factors previously used in other scoring systems and tested it in a cohortof 560 surgical patients undergoing elective or emergency procedures and subsequently admitted to intensive care units,high-dependency units or standard wards. The whole dataset was divided into a training set, to train the predictive model, anda testing set, to assess generalization performance. The effectiveness of the Artificial Neural Network is a measure of theaccuracy in detecting those patients who will develop postoperative complications.A total of 560 surgical patients entered the analysis. Among them, 77 patients (13.7%) suffered from one or more postoperativecomplications (PoCs), while 483 patients (86.3%) did not. The trained Artificial Neural Network returned an average classificationaccuracy of 90% in the testing set. Specifically, classification accuracy was 90.2% in the control group (46 patients out of 51were correctly classified) and 88.9% in the PoC group (8 patients out of 9 were correctly classified).The Artificial Neural Network showed good performance in predicting presence/absence of postoperative complications,suggesting its potential value for perioperative management of surgical patients. Further clinical studies are required to confirmits applicability in routine clinical practice.

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Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/9497388 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Lin Hua ◽

Guangyu Liu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Convolutional Neural Network ◽

Bp Neural Network ◽

Control Group ◽

Gradient Descent Method ◽

Teaching System ◽

Event Classification ◽

Basketball Game ◽

Artificial Neural

The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

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