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.

抽象的 Perceptron Health 提议开发人工智能（AI）软件和图像处理 培训高级实践提供商 (APP) 执行合格的灵活光纤操作的辅助工具 对患者进行喉镜检查 (FFL) 并提高他们对技能的掌握。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 地点。农村地区受影响最大。这种新颖的人工智能工具有可能增加 临床医生能够对 13,000 个 ENT 到近 500,000 个 APP 进行喉镜检查，填补了重症监护的空白 差距。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 充分捕捉患者的任务。然后，录音可以由远程人员审阅和解释 耳鼻喉科医生，让他们专注于诊断和治疗。基于人工智能的产品将包括一张图像 捕获指导系统以及跟踪成功的程序清单和质量检查系统 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该工具包的技术可行性：1. 开发一个原型软件工具包，为喉镜检查过程提供指导； 2. 测试 原型工具能够提高在人体模型上进行喉镜检查的能力； 3. 评估人工智能 能够在预先录制的视频中推广到人体解剖学。 Perceptron 的工具将通过创建执业者辅助工具来扩大患者对 FFL 的访问，该工具能够 识别解剖结构，相对于解剖结构定位相机，并为 用户通过用户界面 (UI)。该项目中要生成的原型需要新颖的开发 通过开发最先进的技术，能够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将人工智能算法集成到喉镜中。这 所提出的算法相对于传统方法可以提供实质性改进，并且将具有 此外，还应用于许多其他医学内窥镜检查环境（胃肠道、肺部等） 处理喉镜视频的图像。一旦完全开发出来，这项创新将允许非耳鼻喉科患者 临床医生扩大实践范围，同时支持耳鼻喉科和言语能力 病理学家可以进行更远程的护理并接触到更多的患者。该技术的其他潜在用户 包括急诊科医生和麻醉师。重要的是，所提出的技术有望改善健康 通过扩大获得专业护理的社会经济机会并减少治疗时间。