AI to detect 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, reveals a new paper presented at the European Respiratory Society International Congress in Barcelona, Spain.

Wafaa Aljbawi, a researcher at the Institute of Data Science, Maastricht University, The Netherlands, claimed that the AI model used in the research 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-19 infection in people who showed no symptoms.

The 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.

“Such tests can be provided at no cost and are simple to interpret. 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.”

COVID-19 infection usually affects the upper respiratory track and vocal cords, leading to changes in a person’s voice. Aljbawi and her supervisors, Dr Sami Simons, pulmonologist at Maastricht University Medical Centre, and Dr Visara Urovi, also from the Institute of Data Science, decided to investigate if it was possible to use AI to analyse voices in order to detect COVID-19.

They used data from the University of Cambridge’s crowd-sourcing COVID-19 Sounds App that contains 893 audio samples from 4352 healthy and non-healthy participants, 308 of whom had tested positive for COVID-19.

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,” Aljbawi said.

“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.”

They found that one model called Long-Short Term Memory (LSTM) outperformed the other models. LSTM is based on neural networks, which mimic the way the human brain operates and recognises the underlying relationships in data. It works with sequences, which makes it suitable for modelling signals collected over time, such as from the voice, because of its ability to store data in its memory.

“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,” Aljbawi said.

“The lateral flow test has a sensitivity of only 56%, but a higher specificity rate of 99.5%. This is important as it signifies that the lateral flow test is misclassifying infected people as COVID-19 negative more often than our test. In other words, with the AI LSTM model, we could miss 11 out 100 cases who would go on to spread the infection, while the lateral flow test would miss 44 out of 100 cases.

“The high specificity of the lateral flow test means that only one in 100 people would be wrongly told they were COVID-19 positive when, in fact, they were not infected, while the LSTM test would wrongly diagnose 17 in 100 non-infected people as positive. However, since this test is virtually free, it is possible to invite people for PCR tests if the LSTM tests show they are positive.”

The researchers say that their results need to be validated with large numbers. Since the start of this project, 53,449 audio samples from 36,116 participants have now been collected and can be used to improve and validate the accuracy of the model. They are also carrying out further analysis to understand which parameters in the voice are influencing the AI model.

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