Automatic Detection of Sports Injuries Based on Multimedia Intelligent 3D Images

Liu, Chunmei; Cao, Longhui

doi:10.1155/2023/4180887

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The experimental results indicated that the method's target detection accuracy requires further improvement. Liu et al examined deep learning techniques for football video analysis, covering player and ball detection, tracking, event detection, and game analysis [114]. Their study contributes to the growing demand for video analysis in sports.…”

Section: Restricted Area Monitoring Explosive Object Monitoringmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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From pivotal roles in autonomous vehicles, healthcare diagnostics, and surveillance systems to seamlessly integrating with augmented reality, object detection algorithms stand as the cornerstone in unraveling the complexities of the visual world. Tracing the trajectory from conventional region-based methods to the latest neural network architectures reveals a technological renaissance where algorithms metamorphose into digital artisans. However, this journey is not without hurdles, prompting researchers to grapple with real-time detection, robustness in varied environments, and interpretability amidst the intricacies of deep learning. The allure of addressing issues such as occlusions, scale variations, and finegrained categorization propels exploration into uncharted territories, beckoning the scholarly community to contribute to an ongoing saga of innovation and discovery. This research offers a comprehensive panorama, encapsulating the applications reshaping our digital reality, the advancements pushing the boundaries of perception, and the open issues extending an invitation to the next generation of visionaries to explore uncharted frontiers within object detection.

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Section: Restricted Area Monitoring Explosive Object Monitoringmentioning

confidence: 99%

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Tanzib Hosain,

Zaman,

Abir

et al. 2024

IEEE Access

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“…Such is the expansion of virtual environments and cybernetics that medicine has been altered and potentially benefited by technological advances [2]. Currently, surgery processes [3], clinical decisions [5,6], and medical treatment [7] are being enhanced with technology based on algorithms that enable technological learning [8]. Recently, ChatGPT (generative pretrained transformer), a chatbot that uses OpenAI's GPT-3 language model, has been introduced, showing interesting results in various medical fields [9,10].…”

Section: Introductionmentioning

confidence: 99%

After a few months, what are the uses of OpenAI’s ChatGPT in medicine? A Scopus-based systematic review

Moya-Salazar,

Salazar,

Delzo

et al. 2024

ELECTRON J GEN MED

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Introduction: Acute ChatGPT, an artificial inteeligence-powered chatbot, presents a potential influence on healthcare and research in medicine. In this investigation, we systematically reviewed medical literature regarding the utilization of ChatGPT, utilizing the Scopus database. Methods: This systematic review follows the preferred reporting items for systematic reviews and meta-analyses guidelines, and according to the search in Scopus, 11,219 articles were identified. Original studies, clinical trials, correspondence articles, letters, notes, and research protocols were included, resulting in 14 studies. Results: All studies were published in 2023, among them 35.7% (5/14) were editorials and 14.3% (2/14) were opinion or perspective articles. Only one study (19) conducted a pilot study on the application of ChatGPT in medical diagnosis. Furthermore, 64.3% of the studies were published in quartile 1 journals according to Scimago rankings, and the USA had the highest production of articles on ChatGPT in medicine (35.7%). The included studies highlighted the potential and challenges of ChatGPT in various domains of healthcare. Ethical concerns, such as data privacy and bias, were also analyzed. Various fields of health were evaluated with ChatGPT, including nuclear medicine, gynecology, nuclear medicine, and emergency medicine. ChatGPT was considered a tool to improve patient education. ChatGPT has a promising role in scientific writing, emphasizing the need for human involvement, ethical guidelines, and addressing concerns related to bias, fabrication, and plagiarism. Conclusions: Scopus-retrieved articles underscored the potential advantages and ethical dilemmas associated with the integration of ChatGPT in healthcare. While ChatGPT demonstrated promising applications in various medical domains, caution, further evaluation, and ethical considerations are needed for responsible implementation in real-world clinical settings.

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“…3D educational images can provide students with an intuitive and interactive learning experience, helping them to better understand complex concepts and processes [4]. Especially in fields such as science, medicine, and engineering, 3D visual materials make abstract theories more visual, enhancing students' spatial cognition abilities, thus showing great application potential and value in the field of education [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

3D Image Modeling and Visual Presentation Technologies for Education

Wang,

Yin,

Zhu

et al. 2024

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With the extensive application of digital media and interactive technologies in the field of education, 3D image modeling and visual presentation technologies have become key tools for enhancing the learning experience. These technologies can concretize abstract educational content, assisting students in understanding complex knowledge through intuitive means. Although current 3D modeling and rendering technologies are widely applied in education, existing methods still need improvement in feature extraction, model expressiveness, and rendering efficiency, particularly in meeting the demands for real-time interaction and providing immersive learning experiences. Addressing this issue, this paper delves into 3D educational image modeling methods based on inter-layer feature learning and explores visual rendering optimization strategies for 3D educational image scenes. By improving the capability of deep learning models to process educational content and optimizing the rendering process to support real-time interaction in large-scale scenes, this research aims to provide more accurate and efficient teaching tools, thereby enhancing the efficiency and quality of teaching and learning. The results indicate that the proposed methods significantly improve model detailing and rendering speed while ensuring visual effects, offering substantial practical significance in enhancing interactivity and immersion in educational technology.

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Automatic Detection of Sports Injuries Based on Multimedia Intelligent 3D Images

Cited by 3 publications

References 22 publications

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

Synchronizing Object Detection: Applications, Advancements and Existing Challenges

After a few months, what are the uses of OpenAI’s ChatGPT in medicine? A Scopus-based systematic review

3D Image Modeling and Visual Presentation Technologies for Education

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