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Cough-based diagnosis for Respiratory Diseases (RDs) using Artificial Intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias Free Network (RBFNet), an end to end solution that effectively mitigates the impact of confounders in the training data distribution. RBFNet ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID19 dataset in this study. This approach aims to enhance the reliability of AI based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks is proposed for the feature encoder module of RBFNet. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (cGAN) which helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBFNet is demonstrated by comparing classification performance with State of The Art (SoTA) Deep Learning (DL) model (CNN LSTM) after training on different unbalanced COVID-19 data sets, created by using a large scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively, Comment: 13 pages, 7 figures, 5 tables

Background: The inability to test at scale has become humanity's Achille's heel in the ongoing war against the COVID-19 pandemic. A scalable screening tool would be a game changer. Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app. The app, named AI4COVID-19 records and sends three 3-second cough sounds to an AI engine running in the cloud, and returns a result within two minutes. Methods: Cough is a symptom of over thirty non-COVID-19 related medical conditions. This makes the diagnosis of a COVID-19 infection by cough alone an extremely challenging multidisciplinary problem. We address this problem by investigating the distinctness of pathomorphological alterations in the respiratory system induced by COVID-19 infection when compared to other respiratory infections. To overcome the COVID-19 cough training data shortage we exploit transfer learning. To reduce the misdiagnosis risk stemming from the complex dimensionality of the problem, we leverage a multi-pronged mediator centered risk-averse AI architecture. Results: Results show AI4COVID-19 can distinguish among COVID-19 coughs and several types of non-COVID-19 coughs. The accuracy is promising enough to encourage a large-scale collection of labeled cough data to gauge the generalization capability of AI4COVID-19. AI4COVID-19 is not a clinical grade testing tool. Instead, it offers a screening tool deployable anytime, anywhere, by anyone. It can also be a clinical decision assistance tool used to channel clinical-testing and treatment to those who need it the most, thereby saving more lives., Comment: Accepted in Informatics in Medicine Unlocked 2020

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models., Comment: Accepted in IEEE International Conference on E-Health and Bioengineering - EHB 2020