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.

该 I-Corps 项目更广泛的影响/商业潜力是远程医疗技术的发展，该技术将护理服务模式从面对面转变为虚拟。尽管最近有大量的医学人工智能（AI）文献，但很少有形式的人工智能能够从理论背景转化为临床实践。所提出的技术通过新颖的深度学习技术增强人工智能性能，评估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会改变抗生素处方实践。这项研究可能会扩展临床实践中使用的基于人工智能的临床决策支持软件的知识。改善虚拟医疗保健的获取可能会影响那些最弱势的人群，例如服务不足的人、缺乏交通的人、残疾人或患有慢性病的人。例如，如果 4000 万次链球菌性咽喉炎就诊中的每一次都通过远程医疗而不是急诊室或紧急护理进行，那么健康保险公司每年可以节省 4-120 亿美元。这个 I-Corps 项目基于开发一种新颖、深入的技术使用智能手机喉咙视频预测链球菌性咽喉炎的学习算法。目前，通过使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，当前的技术使用高质量的单帧图像，在移动的患者中获取这些图像是不切实际的，或者它们使用的硬件对于在家中的患者来说不容易访问。一种新方法是使用智能手机视频进行机器学习。此外，链球菌性咽喉炎还存在独特的视觉变化，例如扁桃体发红或流脓，医生识别的准确度仅为 55%。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（链球菌性咽喉炎）和病毒性咽炎的智能手机图像。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。