I-Corps: Centralized, Cloud-Based, Artificial Intelligence (AI) Video Analysis for Enhanced Intubation Documentation and Continuous Quality Control
I-Corps: Centralized, Cloud-Based, Artificial Intelligence (AI) Video Analysis for Enhanced Intubation Documentation and Continuous Quality Control
批准号:
2405662
负责人:
Ran Yang
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-02-01 至 2025-01-31
中文摘要
这个i-Corps项目的更广泛的影响/商业潜力是开发一个由人工智能(AI)支持的医疗视频分析平台。目前,医疗专业人员经常面临及时报告患者护理的挑战。这项技术旨在使他们能够上传程序视频并立即接收必要的文档。该项目最初的重点是呼吸道管理,包括紧急插管等关键程序。此外,该技术还可以通过加速完成基本文档和质量保证任务来优化工作流程,有效地减轻提供商的负担。这种效率可能会转化为更多的时间用于直接患者护理,改善患者结局,并提高紧急医疗服务的标准。这个i-Corps项目基于开发基于人工智能(AI)的深度学习神经网络,以分析医疗视频并准确输出医疗程序中的事件。这项技术代表着在呼吸道管理技术和人工智能在临床环境中的应用方面的广泛研究的高潮,特别是视频喉镜检查。来自现代视频喉镜的插管视频被用来注释每一帧的呼吸道解剖,最终形成一个全面的数据集。该数据集为神经网络的训练和验证提供了依据。该技术旨在提供一种人工智能解决方案,帮助快速准确地解释和理解复杂的医疗程序。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI)-powered platform for medical video analysis. Currently, medical professionals often face the challenge of timely patient care reporting. This technology is designed to enable them to upload procedure videos and receive immediate, essential documentation. The initial focus of the project is on airway management including critical procedures such as emergency intubation. In addition, the technology may optimize workflow by accelerating the completion of essential documentation and quality assurance tasks, effectively reducing providers’ burdens. This efficiency may translate to more time for direct patient care, improved patient outcomes, and an elevated standard of emergency medical services.This I-Corps project is based on developing an artificial intelligence (AI)-based, deep learning neural network to analyze medical videos and accurately output events in medical procedures. The technology represents the culmination of extensive research in airway management techniques and the application of AI in clinical settings, with a particular focus on video laryngoscopy. Intubation videos from modern video laryngoscopes were used to annotate airway anatomy in every frame, culminating in a comprehensive dataset. This dataset informs the neural network training and validation. The technology is designed to provide an AI solution that assists in interpreting and understanding complex medical procedures both quickly and accurately.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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