i-Diagnosis: Voice-based Artificial Intelligence for COVID-19 Detection

Sound signals produced by the human body are often used as biomarkers for diagnosing and monitoring disease. The team used machine learning to develop an accurate and interpretable COVID-19 diagnostic method model based on human speech, cough, and breathing data, with a detection accuracy rate of up to 70%. Based on this model, the team will target all citizens to create a mobile phone application for diagnosis, aiming to achieve low-cost, low-risk, high-accuracy, and high-efficiency initial screening of COVID-19. At the same time, the team is also committed to collecting national data on COVID-19 and collaborating with governments or relevant pharmaceutical companies, hoping to develop diagnostics for other diseases with voice characteristics and extend it to the screening and tests of more common respiratory diseases.