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.

I-Corps项目的更广泛影响/商业潜力是开发远程保健技术，将护理提供模式从面对面转变为虚拟。尽管最近医学人工智能（AI）文献丰富，但很少有形式的人工智能从理论背景转化为临床实践。拟议的技术通过新颖的深度学习技术增强了人工智能的性能，评估了人工智能医学测试在临床实践中的可用性和可解释性，并衡量人工智能测试的使用是否改变了抗生素处方实践。本研究可以扩展临床实践中基于人工智能的临床决策支持软件的知识。改善对虚拟医疗保健的访问可以影响最脆弱的人群，例如服务不足的人群、交通不便的人群、残疾人或患有慢性疾病的人群。例如，如果4000万次链球菌性喉炎就诊中的每一次都是通过远程医疗而不是急诊室或紧急护理进行的，医疗保险公司每年可以节省40 - 120亿美元。这个I-Corps项目是基于一种新颖的深度学习算法的开发，该算法可以通过智能手机的喉咙视频来预测链球菌性喉炎。目前，利用扁桃体的模式识别和基本的机器学习，从智能手机照片中检测链球菌性咽喉炎是可行的。然而，目前的技术使用的是高质量的单帧图像，这些图像在移动的病人身上是不切实际的，或者他们使用的硬件对在家的病人来说不容易获得。一种新的方法是使用智能手机视频进行机器学习。此外，链球菌性咽喉炎还有一些独特的视觉变化，比如扁桃体发红或脓，医生只能识别出55%的准确率。该技术应用这些视觉变化的模式识别来区分链球菌性咽炎和病毒性咽炎的智能手机图像。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。