A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research

Objectives: Comparison of three different Artificial intelligence (AI) methods of assessment for patients undergoing Computed tomography (CT) for suspected Covid-19 disease. Parameters studied were probability of diagnosis, quantification of disease severity and the time to reach the diagnosis . Methods: 107 consecutive patients of suspected Covid-19 patients were evaluated using the three AI methods labeled as AI-I,II, III alongwith visual analysis labeled as VT for predicting probability of Covid-19, determining CT severity score (CTSS) and index (CTSI) , percentage opacification (PO) and high opacification (POHO). Sensitivity, specificity along with area under curves were estimated for each method and the CTSS and CTSI correlated using Friedman test. Results: Out of 107 patients 71 patients were Covid-19 positive and 20 negative by RT-PCR while 16 did not get RT-PCR done. AI-III method showed higher sensitivity and specificity of 93% and 88% respectively to predict probability of Covid 19. It had 2 false positive patients of interstitial lung disease. AI-II method had sensitivity and specificity of 66% and 83% respectively while visual (VT) analysis showed sensitivity and specificity of 59.7% and 62% respectively. Statistically significant differences were also seen in CTSI and PO estimation between AI-I and III methods (p<0.0001) with AI-III showing fastest time to calculate results. Conclusions: AI-III method gave better results to make an accurate and quick diagnosis of the Covid-19 with AUC of 0.85 to predict probability of Covid-19 alongwith quantification of Covid-19 lesions in the form of PO, POHO as compared to other AI methods and also by visual analysis.

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Artificial intelligence based on effectiveness of inverted classroom teaching of college sports

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189409 ◽

2020 ◽

pp. 1-11

Author(s):

Jianqin Cheng ◽

Xiaomeng Wang

Keyword(s):

Artificial Intelligence ◽

Physical Education ◽

Real Time ◽

Visual Analysis ◽

Analytical Framework ◽

Colleges And Universities ◽

Classroom Teaching ◽

Teaching Behaviors ◽

Inverted Classroom ◽

Artificial Intelligence Technology

This study takes the effectiveness analysis of inverted classroom teaching in colleges and universities as a breakthrough point, and combines artificial intelligence technology with the analysis method of inverted classroom teaching in colleges and universities to enrich the existing methods for analyzing, the behavior of inverted classroom teaching in colleges and universities to realize the effectiveness of inverted classroom teaching in colleges and universities analysis. This research first constructs an analytical framework for the teaching behaviors of college physical education inverted classrooms based on artificial intelligence technology, which consists of observation dimension and the evaluation dimension. In order to further test the scientifically and operability of the analytical framework, taking emotion recognition as an example, practical operations are combined with specific examples to obtain visual analysis results. This study expands the dimension and depth of analysis of the behavior of inverted sport in classroom teaching in sport inversion colleges and universities, and has obvious advantages in saving manpower and real-time visual display. Through the analysis of the effectiveness of physical education inverted classroom teaching in sports inversion colleges and universities through artificial intelligence technology, the use of technology to participate in the analysis of physical education inverted classroom teaching behaviors in sports inverted colleges and universities, shorten the evaluation time, expand the evaluation dimension, improve the evaluation efficiency, achieve real-time feedback, real-time attention to classroom effects. Effectively regulating the inverted classroom teaching behavior of college physical education can promote the cultivation of teachers’ professional abilities, scientifically and accurately improve and correct teaching problems, and improve the quality of education and teaching. Eventually, students will achieve comprehensive self-evaluation of students, and promote personalized and standardized growth of students.

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Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia

Computational Intelligence and Neuroscience ◽

10.1155/2020/8876798 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Zhenjia Yue ◽

Liangping Ma ◽

Runfeng Zhang

Keyword(s):

Artificial Intelligence ◽

Visual Analysis ◽

Clinical Image ◽

Human Beings ◽

Accuracy Rate ◽

Network Algorithms ◽

X Ray ◽

Image Diagnosis ◽

Chest X Ray ◽

Low Efficiency

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor’s clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet’s network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet’s network could also extend to other areas for application.

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Traversing Anthropocentric Horizons With Her: Trans-Corporeal Surrogacy, Enchantment, and Disenchantment in Human-Machine Assemblage

Journal of Communication Inquiry ◽

10.1177/01968599211041107 ◽

2021 ◽

pp. 019685992110411

Author(s):

Mariam Betlemidze

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

Romantic Relationship ◽

Visual Analysis ◽

Human Capabilities ◽

In Trans ◽

Audio Communication ◽

Autonomous Agency ◽

Machine Communication ◽

Shed Light

This article aims to shed light on the intricacies that overturn McLuhan's vision of technologies as extensions or prosthetics of human capabilities when applied to human-machine communication (HMC). Human and nonhuman entities co-evolve on an equal agential footing, immersed in mediatized assemblages. Building on the concepts of Deleuze and Guattari, Bennett, and others, it theorizes HMC as a cycle of sonic enchantment, culminating in trans-corporeal surrogacy, disrupted by disenchantment, and started again through re-enchantment. A new materialist framework helps explain the process of posthuman HMC. It provides a close-textual and visual analysis of Spike Jonze's film Her (2013), in which a human develops a romantic relationship with his AI assistant. The aspects of vulnerability, neediness, authenticity, trust, and intimacy surpass the lure of real-time personalized audio communication. The paper argues that artificial intelligence acquires autonomous agency through the processes of enchantment and mutual surrogacy that decenter humans in mediatized assemblages.

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Visual Analysis of the Application of Artificial Intelligence in Education

2018 International Joint Conference on Information, Media and Engineering (ICIME) ◽

10.1109/icime.2018.00046 ◽

2018 ◽

Cited By ~ 1

Author(s):

Fei Wang ◽

Xinrong Tao

Keyword(s):

Artificial Intelligence ◽

Visual Analysis ◽

Artificial Intelligence In Education

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Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI

Radiology Artificial Intelligence ◽

10.1148/ryai.2021190169 ◽

2021 ◽

Vol 3 (3) ◽

pp. e190169

Author(s):

Christoph Baur ◽

Benedikt Wiestler ◽

Mark Muehlau ◽

Claus Zimmer ◽

Nassir Navab ◽

...

Keyword(s):

Artificial Intelligence ◽

Anomaly Detection ◽

Brain Mri ◽

Unsupervised Anomaly Detection

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Multi-parametric MRI phenotype with trustworthy machine learning for differentiating CNS demyelinating diseases

Journal of Translational Medicine ◽

10.1186/s12967-021-03015-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Jing Huang ◽

Bowen Xin ◽

Xiuying Wang ◽

Zhigang Qi ◽

Huiqing Dong ◽

...

Keyword(s):

Differential Diagnosis ◽

Cross Validation ◽

Visual Analysis ◽

Brain Mri ◽

White Matter Lesions ◽

Demyelinating Diseases ◽

Imaging Biomarkers ◽

Training Set ◽

Brain White Matter ◽

Fold Cross Validation

Abstract Background Misdiagnosis of multiple sclerosis (MS) and neuromyelitis optica (NMO) may delay the treatment, resulting in poor prognosis. However, the precise identification of these two diseases is still challenging in clinical practice. We aimed to evaluate the value of quantitative radiomic features extracted from the brain white matter lesions for differential diagnosis of MS and NMO. Methods We recruited 116 CNS demyelinating patients including 78 MS, and 38 NMO. Three neuroradiologists performed visual differential diagnosis based on brain MRI for comparison purpose. A multi-level scheme was designed to harness the selection of discriminative and stable radiomics features extracted from brain while mater lesions in T1-MPRAGE, T2 sequences and clinical factors. Based on the imaging phenotype composed of the selected radiomic and clinical features, Multi-parametric Multivariate Random Forest (MM-RF) model was constructed and verified with both 10-fold cross-validation and independent testing. Result interpretation was provided to build trust in diagnostic decisions. Results Eighty-six patients were randomly selected to form the training set while the rest 30 patients for independent testing. On the training set, our MM-RF model achieved accuracy 0.849 and AUC 0.826 in 10-fold cross-validation, which were significantly higher than clinical visual analysis (0.709 and 0.683, p < 0.05). In the independent testing, the MM-RF model achieved AUC 0.902, accuracy 0.871, sensitivity 0.873, specificity 0.869, respectively. Furthermore, age, sex and EDSS were found mildly correlated with the radiomic features (p of all < 0.05). Conclusions Multi-parametric radiomic features have potential as practical quantitative imaging biomarkers for differentiating MS from NMO.

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A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.02.010 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-15

Author(s):

Shruti Agarwal ◽

Keyword(s):

Artificial Intelligence ◽

Knowledge Engineering ◽

Brain Mri ◽

Research Community ◽

Research Subject ◽

Engineering Research ◽

Artificial Intelligence Techniques ◽

The Past ◽

History Of ◽

Medical Domain

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.

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