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

这个I-Corps项目更广泛的影响/商业潜力是使用音频人工智能（AI）算法开发呼吸系统疾病诊断。 拟议的技术旨在部署为智能手机应用程序，使患者能够自我诊断COVID-19和其他呼吸道疾病（例如，哮喘、肺结核、流感、肺癌），在家里，匿名，并在几秒钟内通过自愿咳嗽声音的声音分析。该软件应用程序可以改善全球健康，并通过直接为患者提供家庭，非接触，低成本的呼吸道疾病预筛查来改变诊断测试的前景，从而预防未来的流行病。 此外，这也可以为公共卫生部门和私人医疗保险公司节省大量成本，以便在其患者人群中进行早期疾病检测。该技术还可用于制药公司测量咳嗽疾病治疗的有效性，流行病学家和研究人员可以采用这种方法来检索大量人群的实时匿名跟踪疾病状态。这个I-Corps项目是基于开发用于呼吸系统疾病诊断的音频人工智能（AI）算法。所提出的技术源于研究，该研究产生了一种复杂的限幅技术，通过音频信号特征提取和机器学习从咳嗽声中检测COVID-19。通过一系列临床研究，对从聚合酶链反应（PCR）测试的COVID-19患者收集的咳嗽数据进行了训练。这项工作表明，仅从咳嗽声中诊断COVID-19是可能的，其可靠性类似于抗原检测，具有高性能指标（84%的灵敏度，84%的特异性，0.93的曲线下面积（AUC））。预计该技术可以支持对其他疾病（例如，哮喘，肺结核，流感，肺癌），通过利用人工智能迁移学习对来自20个国家的415，406名PCR测试患者的咳嗽/言语声音的大规模数据集进行训练数据较少。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准确性（例如，该奖项反映了NSF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.