I-Corps: Detection of Strep Throat on a Telehealth Visit using Artificial Intelligence (AI) and Smartphone Images

I-Corps：使用人工智能 (AI) 和智能手机图像在远程医疗就诊中检测链球菌性咽喉炎

• 批准号: 2231883
• 负责人: Keith Kleinman
• 金额: $ 5万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231883&HistoricalAwards=false
• 关键词: Corps; Detection; Strep; Throat; Telehealth

The broader impact/commercial potential of this I-Corps project is the development of telehealth technology that shifts the care delivery model from in-person to virtual. Despite the wealth of recent medical artificial intelligence (AI) literature, few forms of AI translate from a theoretical context into clinical practice. The proposed technology enhances AI performance with novel, deep learning techniques, evaluates the usability and interpretability of an AI medical test in clinical practice, and measures whether use of the AI test alters antibiotic prescribing practices. This research may expand the knowledge around AI-based clinical decision support software used in clinical practice. Improved access to virtual healthcare can impact those who are most vulnerable, such as the underserved, those who lack transportation, those who have disabilities, or tjpse who have chronic illness. For example, if each of the 40 million strep throat visits were performed on telehealth instead of emergency rooms or urgent cares, health insurers could save $4-12 billion/year.This I-Corps project is based on the development of a novel, deep learning algorithm that predicts strep throat using a smartphone video of the throat. It is currently feasible to detect strep throat from a smartphone photo by using pattern recognition of the tonsils and basic machine learning. However, the current technologies use high quality, single frame images that are impractical to acquire in a moving patient, or they use hardware not easily accessible to patients at home. A new approach is to use smartphone video for machine learning. Furthermore, there are visual changes unique to strep throat, such as redness or pus on the tonsils, that doctors can only identify with 55% accuracy. This technology applies pattern recognition of these visual changes to differentiate smartphone images of streptococcal pharyngitis (strep throat) from viral pharyngitis.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.

这个I-Corps项目的更广泛的影响/商业潜力是远程医疗技术的开发，该技术将护理交付模式从面对面转变为虚拟。尽管最近的医学人工智能（AI）文献丰富，但很少有AI从理论上下文转化为临床实践。提出的技术通过新颖，深度学习技术增强了AI性能，评估了在临床实践中AI医学测试的可用性和解释性，并衡量AI测试的使用是否改变了抗生素处方实践。这项研究可能会扩大临床实践中使用的基于AI的临床决策支持软件的知识。改善获得虚拟医疗保健可能会影响最脆弱的人，例如服务不足的人，缺乏交通工具，残疾人或患有慢性病的TJPSE。例如，如果在远程医疗上而不是急诊室或紧急护理上进行了4000万个链球菌喉咙探访中的每一个，那么健康保险公司可以节省4-120亿美元/年。这项I-Corps项目基于一种新颖，深度学习算法的开发，该算法使用喉咙的智能手机视频来预测链球菌喉咙。目前，通过使用扁桃体和基本机器学习的模式识别，可以从智能手机照片中检测出链球菌喉咙。但是，当前的技术使用高质量的单帧图像，这些图像在移动的患者中获取不切实际，或者他们使用的硬件在家中不容易获得。一种新方法是将智能手机视频用于机器学习。此外，链球菌性喉咙有独特的视觉变化，例如扁桃体上的发红或脓液，医生只能以55％的精度识别。该技术对这些视觉变化进行了模式识别，以区分病毒性咽炎的链球菌性咽炎（链球菌性喉咙）的智能手机图像。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。