Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Zhang, Ying; Feng, Hongbo; Zhao, Yan; Zhang, Shuo

doi:10.3390/diagnostics14020146

Cited by 8 publications

(3 citation statements)

References 56 publications

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“…RESULT AND DISCUSSION Following an exploration of the methodologies employed in this research, our focus now shifts to presenting and analyzing the results obtained through the utilization of the Learning Focal Point (LFP) algorithm. A key aspect of this analysis involves a comparative examination with the outcomes derived from employing the widely utilized MaxPooling technique [67], [68], [69]. This comparative evaluation aims to underscore the significance of the Learning Focal Point algorithm in enhancing the performance of neural networks.…”

Section: Fig 3 Points Of Difference Between the Female And Male Bodymentioning

confidence: 99%

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Sakhi,

Mansour,

Sekkaki

2024

Journal of Robotics and Control

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Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.

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Section: Fig 3 Points Of Difference Between the Female And Male Bodymentioning

confidence: 99%

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Sakhi,

Mansour,

Sekkaki

2024

Journal of Robotics and Control

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“…Matthew believes the instructional stage is an organic whole composed of many variable factors, among which the most important factors are teachers, classmates, tutorials, learning environment, lecture methods, and classroom feedback. All these factors affect the quality of teaching in different forms and degrees (Zhang et al, 2024). Therefore, some important factors should be included in the instructional assessment (Rahiman & Kodikal, 2024).…”

Section: Literature Reviewmentioning

confidence: 99%

Construction of a Multimedia Instructional Effect Assessment System of College Foreign Language Translation Based on Artificial Intelligence

Liu,

2024

International Journal of Information Systems and Supply Chain Management

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The teaching evaluation module is an important guarantee of teaching effectiveness, and as an important part of the multimedia teaching system, it needs special attention. This paper combines artificial intelligence and multimedia teaching effect evaluation, proposes a foreign language translation multimedia teaching effect evaluation model based on improved support vector machine (SVM) algorithm, and provides theoretical support for the construction of foreign language translation multimedia teaching effect evaluation system. The results show that the accuracy of this assessment algorithm is improved by 22.18% compared with the traditional assessment algorithm. Therefore, it is theoretically feasible to use the improved SVM to analyse the application effect of multimedia teaching mode in foreign language translation teaching. It can be found that the automatic acquisition of model data, the cumulative search of spatial knowledge and the adaptive control of the search phase of the optimised SVM algorithm have also been significantly improved, and conclusions drawn are more reliable.

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“…Recent, reviews have explored the current and potential applications of AI in radiography practice [22], and examined the AI educational programmes available to radiography staff globally [34]. Moreover, reviews related to specific AI tools such as ChatGPT [35], open-source software for medical image processing [36], and the use of immersive virtual reality in undergraduate radiography curricula have also been published recently [37,38]. Furthermore, previous research done in Europe, Africa, and the Middle East exploring AI topics linked to education in radiography has focused on equipping practitioners [2] as well as the perceptions and attitudes of students and practitioners [39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in radiography education: A scoping review protocol. (Preprint)

Kammies,

Archer,

Engel-Hills

et al. 2024

Preprint

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BACKGROUND The expansion of Artificial Intelligence (AI) in radiography is predicted to transform future clinical workflows, decision-making, and the role and responsibilities of practitioners. If AI is to be an important aspect of radiography practice it may be reasonable to expect that graduates need to be educated and equipped to engage in competent and safe utilisation of these technologies. Therefore, the current and emerging status of AI in radiography education needs to be explored. OBJECTIVE The purpose of this scoping review is to map the existing literature on AI applications in radiography education and how AI technologies will potentially impact radiography education. The review will identify the various influences of AI in academic and clinical education to inform future radiography curricula. METHODS Using the Johanna Briggs Institute methodology, an initial search was run in EBSCOhost to determine whether the search strategy that was developed with a library specialist would capture the relevant literature by screening the title and abstract of the first 50 articles. Any additional search terms identified in the articles were then added to the search strategy. Thereafter, PubMed, Scopus, and Web of Science databases were searched. Also, grey literature was sourced from ProQuest and relevant institutional repositories. Abstract and full-text articles will be independently reviewed by two reviewers according to the predefined inclusion and exclusion criteria. Thereafter, search results will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). RESULTS The database searches were concluded in April 2024 and yielded 2 827 results. The final scoping review will present the findings in tabular form and through narrative descriptions. CONCLUSIONS This review will provide a better understanding of the AI applications in radiography education and how AI tools impact radiography education. CLINICALTRIAL https://doi.org/10.17605/OSF.IO/3NX2A

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Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Cited by 8 publications

References 56 publications

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Construction of a Multimedia Instructional Effect Assessment System of College Foreign Language Translation Based on Artificial Intelligence

Artificial Intelligence in radiography education: A scoping review protocol. (Preprint)

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