Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough

TL;DR

This paper presents a global AI-based COVID-19 detection method using crowdsourced cough audio data, demonstrating promising accuracy and generalization across diverse regions without region-specific training.

Contribution

It introduces a novel approach leveraging crowdsourced cough audio for COVID-19 detection that generalizes across different geographic populations without additional region-specific data.

Findings

Achieved ROC-AUC of 77.1% in COVID-19 prediction.

Generalized well to Latin American and South Asian samples.

Potential for global, accessible COVID-19 screening.

Abstract

Rapid and affordable methods of testing for COVID-19 infections are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed. Current approaches of detecting COVID-19 require in-person testing with expensive kits that are not always easily accessible. This study demonstrates that crowdsourced cough audio samples recorded and acquired on smartphones from around the world can be used to develop an AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1% (75.2%-78.3%). Furthermore, we show that our method is able to generalize to crowdsourced audio samples from Latin America and clinical samples from South Asia, without further training using the specific samples from those regions. As more crowdsourced data is collected, further development can be implemented using various respiratory audio samples to create a cough analysis-based machine learning (ML) solution for COVID-19 detection that can likely generalize globally to all demographic groups in both clinical and non-clinical settings.