I-Corps: Audio Artificial Intelligence Data Platform to Diagnose Respiratory Disease

I-Corps项目的更广泛影响/商业潜力是开发使用音频人工智能（AI）算法的呼吸系统疾病诊断。这项拟议中的技术旨在作为一款智能手机应用程序部署，使患者能够通过对自主咳嗽声音的声音分析，在家、匿名、几秒钟内自我诊断COVID-19和其他呼吸道疾病（如哮喘、结核病、流感、肺癌）。该软件应用程序可以改善全球健康状况，并通过改变诊断测试的格局，将无接触、低成本的呼吸系统疾病预先筛查直接带到患者身上，从而预防未来的流行病。此外，这也可能为公共卫生部门和私营医疗保险公司节省大量费用，以便在其患者群体中扫描早期发现疾病。该技术也可以被制药公司用来衡量咳嗽引起的疾病的治疗效果，流行病学家和研究人员可以采用这种方法来实时、匿名地追踪大量人群的疾病状况。I-Corps项目的基础是开发用于呼吸道疾病诊断的音频人工智能（AI）算法。该技术源于通过音频信号特征提取和机器学习，从咳嗽声中检测COVID-19的复杂剪辑技术的研究成果。该算法是通过一系列临床研究收集的聚合酶链反应（PCR）检测的COVID-19患者的咳嗽数据进行训练的。这项工作表明，仅从咳嗽声中诊断COVID-19是可能的，其可靠性与抗原检测相似，具有很高的性能指标（84%的灵敏度，84%的特异性，0.93的曲线下面积（AUC））。预计该技术可以通过对来自20个国家的415,406名pcr检测患者的咳嗽/语音的大规模数据集利用人工智能迁移学习，以较少的训练数据支持对其他疾病（例如哮喘、结核病、流感、肺癌）的强大检测。此外，初步结果表明，复杂裁剪技术提高了与其他上下文相关的其他噪声的分类精度（例如水声噪声的自动分类）。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。

The broader impact/commercial potential of this I-Corps project is the development of a respiratory disease diagnostic using an audio artificial intelligence (AI) algorithm. The proposed technology is designed to be deployed as a smartphone app that would enable patients to self-diagnose COVID-19 and other respiratory diseases (e.g., asthma, tuberculosis, flu, lung cancer), at-home, anonymously, and in seconds through sound analysis of voluntary cough sounds. This software application may improve global health, and prevent future pandemics by changing the landscape of diagnostic testing by bringing at-home, contact-less, low-cost pre-screening for respiratory illness directly to patients. In addition, this also may result in significant cost savings for public health departments and private medical insurers scanning for early detection of diseases in their patient populations. The proposed technology also may be used by pharmaceutical companies to measure the effectiveness of therapies for cough producing illnesses and epidemiologists and researchers could adopt this method to retrieve real-time, anonymized tracking of disease status of large populations.This I-Corps project is based on the development of audio artificial intelligence (AI) algorithms for respiratory disease diagnostics. The proposed technology originates from research resulting in a complex clipping technique to detect COVID-19 from cough sound through audio signal feature extraction and machine learning. The proposed algorithm was trained on cough data collected from polymerase chain reaction (PCR)-tested COVID-19 patients through a series of clinical research studies. This work has shown that it is possible to diagnose COVID-19 from the cough sound alone with reliability similar to antigen testing, at high performance metrics (84% sensitivity, 84% specificity, 0.93 area under the curve (AUC)). It is anticipated that the technology can support robust detection of other diseases (e.g., asthma, tuberculosis, flu, lung cancer) with fewer training data by leveraging AI transfer learning on the massive dataset of cough/speech sounds from 415,406 PCR-tested patients across 20 countries. Additionally, preliminary results show that the complex clipping technique improves classification accuracy of other noises relevant for other contexts (e.g., automatic classification of underwater acoustic noises).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.