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

这个i-Corps项目的更广泛的影响/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。这项拟议的技术旨在以智能手机应用的形式部署，使患者能够通过对自愿咳嗽声音的声音分析，在家中匿名在几秒钟内自我诊断新冠肺炎和其他呼吸系统疾病(如哮喘、结核病、流感、肺癌)。这一软件应用程序可以通过将在家、非接触、低成本的呼吸道疾病预筛查直接带给患者，从而改变诊断测试的格局，从而改善全球健康，并防止未来的大流行。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成本，以便在其患者群体中及早发现疾病。拟议的技术也可以被制药公司用来衡量治疗咳嗽疾病的有效性，流行病学家和研究人员可以采用这种方法来检索实时、匿名跟踪大量人群的疾病状态。这个i-Corps项目基于用于呼吸系统疾病诊断的音频人工智能(AI)算法的开发。该技术源于一项研究，该研究产生了一种复杂的剪辑技术，通过音频信号特征提取和机器学习从咳嗽音中检测新冠肺炎。通过一系列临床研究，对从经聚合酶链式反应检测的新冠肺炎患者收集的咳嗽数据进行了训练。这项工作表明，在高性能指标(84%的敏感性，84%的特异性，0.93的曲线下面积(AUC))下，仅凭咳嗽声就可以诊断新冠肺炎，其可靠性类似于抗原检测。预计该技术可以通过在来自20个国家/地区的415,406名经PCR测试的患者的海量咳嗽/语音数据集上利用AI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)的稳健检测。此外，初步结果表明，复杂裁剪技术提高了与其他背景相关的其他噪声的分类精度(例如，水声噪声的自动分类)。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.