An AI assistance tool to guide novice practitioners in the competent performance of flexible video laryngoscopy

人工智能辅助工具，指导新手熟练执行柔性视频喉镜

• 批准号: 10602717
• 负责人: Nasir Islam Bhatti
• 金额: $ 27.57万
• 依托单位: PERCEPTRON HEALTH, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2024-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10602717
• 关键词: Address; Algorithms; Anatomy; Anterior; Artificial Intelligence; Awareness; Caring; Computer software; Cost of Illness; County; Critical Care; Cyst; Data; Data Set; Databases; Development; Diagnosis; Diagnostic Procedure; Disease Progression; Ear; Emergency Department Physician; Endoscopy; Ensure; Feeds; Geographic Locations; Geography; Health; Health Services Accessibility; Healthcare Systems; Human; Image; Interview; Knowledge; Language; Laryngoscopes; Laryngoscopy; Larynx; Lung; Malignant Neoplasms; Manikins; Measurement; Medical; Modeling; Neural Network Simulation; Nodule; Nose; Pathologist; Pathology; Patients; Performance; Pharyngeal structure; Phase; Physicians; Polyps; Population; Procedures; Provider; Quality of life; Research; Risk; Sales; Software Tools; Speech; System; Technology; Testing; Time; Tissues; Training; Work; algorithm development; artificial intelligence algorithm; base; burden of illness; convolutional neural network; cost; diagnostic value; disparity reduction; flexibility; gastrointestinal; health care disparity; health plan; image processing; improved; innovation; interpatient variability; medical specialties; mortality; novel; object recognition; patient population; prototype; remote health care; rural area; skills; socioeconomics; software development; tool; uptake

Abstract Perceptron Health proposes to develop an artificial intelligence (AI) software and image processing assistance tool that trains advanced practice providers (APPs) to perform a competent flexible fiberoptic laryngoscopy (FFL) on patients and improve uptake of their skills. With 65.7% of U.S. counties lacking a practicing ear, nose, and throat physician (ENT), this has led to a disparity in care based on geographical location. Those in rural areas are most impacted. The novel AI tool has the potential to increase the pool of clinicians capable of performing laryngoscopy from 13,000 ENTs to almost 500,000 APPs, filling a critical care gap. The toolkit will guide APP users through the laryngoscopy procedure to ensure all anatomical structures and patient tasks are sufficiently captured. A recording can then be reviewed and interpreted remotely by an ENT physician, allowing them to focus on diagnosis and treatment. The AI-based product will include an image capture guidance system as well as a procedure checklist and quality check system that tracks successful capture of diagnosable views of key anatomical structures. Perceptron Health plans to assess technical feasibility of the toolkit through the following Phase I Objectives: 1. Develop a prototype software toolkit that provides guidance through the laryngoscopy procedure; 2. Test the prototype tool’s capability to improve the ability to perform laryngoscopy on manikins; and 3. Assess the AI’s ability to generalize to human anatomy in pre-recorded video. Perceptron’s tool will expand patient access to FFLs via the creation of a practitioner assistance tool able to identify anatomical structures, localize the camera relative to anatomical structures, and provide guidance to the user through a user interface (UI). The prototype to be generated in this project will require the novel development of algorithms capable of classifying images from laryngoscopy videos through the development of state-of-the- art convolutional neural networks that will allow for the integration of AI algorithms into laryngoscopes. The proposed algorithms can provide substantial improvements relative to conventional approaches and will have application in numerous other medical endoscopy contexts (gastrointestinal, pulmonary, and others) in addition to processing images from laryngoscopy videos. Once fully developed, this innovation will allow non-ENT clinicians to expand their scope of practice while supporting the ability of both ENTs and speech language pathologists to perform more remote care and reach more patients. Other potential users of the technology include ER physicians and anesthesiologists. Importantly, the proposed technology is expected to improve health by expanding socioeconomic access to specialty care and decreasing time to treatment.

摘要 Perceptron Health提出开发人工智能（AI）软件和图像处理 一种帮助工具，用于培训高级实践提供者（APP）， 喉镜检查（FFL）对病人和提高他们的技能吸收。美国65.7%的县缺乏 执业耳鼻喉科医生（ENT），这导致了基于地理的护理差异 位置.农村地区受影响最大。新的人工智能工具有可能增加 能够进行喉镜检查的临床医生从13，000名ENT到近500，000名APP，填补了重症监护 间隙该工具包将指导APP用户完成喉镜检查程序，以确保所有解剖结构 并且患者任务被充分捕获。然后，记录可以被远程地审阅和解释， 耳鼻喉科医生，让他们专注于诊断和治疗。基于AI的产品将包括一个图像 捕获指导系统以及程序检查表和质量检查系统， 捕获关键解剖结构的可诊断视图。 Perceptron Health计划通过以下第一阶段目标评估工具包的技术可行性：1. 开发一个原型软件工具包，通过喉镜检查程序提供指导; 2.测试 原型工具提高在人体模型上进行喉镜检查能力的能力;以及3.评估AI 在预先录制的视频中概括人体解剖学的能力。 Perceptron的工具将通过创建一个能够 识别解剖结构，相对于解剖结构定位摄像机，并提供对解剖结构的引导。 用户通过用户界面（UI）。在这个项目中产生的原型将需要新的发展 能够通过发展最新技术对喉镜视频中的图像进行分类的算法 艺术卷积神经网络，将允许人工智能算法集成到喉镜。的 所提出的算法可以提供相对于传统方法的实质性改进，并且将具有 此外，还可应用于许多其他医学内窥镜检查环境（胃肠道、肺部等） to processing处理images图像from largeoscopy喉镜videos视频.一旦完全开发，这项创新将允许非耳鼻喉科 临床医生扩大他们的实践范围，同时支持ENT和言语语言的能力 病理学家进行更多的远程护理，接触更多的病人。该技术的其他潜在用户 包括急诊室医生和麻醉师。重要的是，这项技术有望改善健康状况。 通过扩大专科护理的社会经济可及性和缩短治疗时间。