Mobile phone app accurately detects COVID-19 infection in people’s voices

Artificial intelligence (AI) can be used to detect COVID-19 infection in people’s voices by means of a mobile phone app, according to research to be presented on Monday at the European Respiratory Society International Congress in Barcelona, Spain [1].

The AI model used in this research is more accurate than lateral flow/rapid antigen tests and is cheap, quick and easy to use, which means it can be used in low-income countries where PCR tests are expensive and/or difficult to distribute.

Ms Wafaa Aljbawi, a researcher at the Institute of Data Science, Maastricht University, The Netherlands, told the congress that the AI model was accurate 89% of the time, whereas the accuracy of lateral flow tests varied widely depending on the brand. Also, lateral flow tests were considerably less accurate at detecting COVID infection in people who showed no symptoms.

COVID-19 infection usually affects the upper respiratory track and vocal cords, leading to changes in a person’s voice.

Covid/commons.wikimedia.org

“These promising results suggest that simple voice recordings and fine-tuned AI algorithms can potentially achieve high precision in determining which patients have COVID-19 infection,” she said.Moreover, they enable remote, virtual testing and have a turnaround time of less than a minute. They could be used, for example, at the entry points for large gatherings, enabling rapid screening of the population.”

The app is installed on the user’s mobile phone, the participants report some basic information about demographics, medical history and smoking status, and then are asked to record some respiratory sounds. These include coughing three times, breathing deeply through their mouth three to five times, and reading a short sentence on the screen three times.

The researchers used a voice analysis technique called Mel-spectrogram analysis, which identifies different voice features such as loudness, power and variation over time.

“In this way we can decompose the many properties of the participants’ voices,” said Ms Aljbawi. “In order to distinguish the voice of COVID-19 patients from those who did not have the disease, we built different artificial intelligence models and evaluated which one worked best at classifying the COVID-19 cases.”

Its overall accuracy was 89%, its ability to correctly detect positive cases (the true positive rate or “sensitivity”) was 89%, and its ability to correctly identify negative cases (the true negative rate or “specificity”) was 83%.

“These results show a significant improvement in the accuracy of diagnosing COVID-19 compared to state-of-the-art tests such as the lateral flow test,” said Ms Aljbawi.

The patients were “high engagers”, who had been using the app weekly over months or even years to record their symptoms and other health information, record medication, set reminders, and have access to up-to-date health and lifestyle information. Doctors can assess the data via a clinician dashboard, enabling them to provide oversight, co-management and remote monitoring.