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Since the onset of the coronavirus pandemic, school districts and public health experts have sought to solve a key missing link for safe in-person learning: how to identify asymptomatic COVID-19 cases among students and staff.

Asymptomatic carriers might come into school buildings and transmit the virus unknowingly, officials worry. In Los Angeles, the district superintendent recently announced that after data from their testing program revealed that 5 percent of adults and nearly 10 percent of youth, who did not report any COVID-19 exposure or symptoms, tested positive for the virus.

Now, new research from the Massachusetts Institute of Technology may provide a solution.

In a paper recently published in the IEEE Journal of Engineering in Medicine and Biology, an MIT research team found that simply by the sound of one’s cough recorded through a smartphone speaker 鈥斅爀ven for asymptomatic individuals.

Though the differences between the forced coughs of healthy and asymptomatic individuals are not discernable to the human ear, it turns out they can be picked up by technology. After researchers Brian Subirana, Jordi Laguarta, and Ferran Hueto trained their model on tens of thousands of sample coughs and spoken words, they tested it on 1,064 subjects, finding that the model accurately identified 98.5 percent of coughs from people who were confirmed to have COVID-19, including 100 percent of coughs from asymptomatic individuals.

鈥淲e think this shows that the way you produce sound changes when you have COVID, even if you鈥檙e asymptomatic,鈥� co-author Subirana, a research scientist in MIT鈥檚 Auto-ID Laboratory, told the .

The MIT team is now working to incorporate their model into an app, which if approved by the FDA could provide a free, non-invasive pre-screening tool to help identify people who have COVID-19 鈥� even those who are not experiencing symptoms. Their hope is that users would be able to log into the app daily, record their cough, and instantly receive information on whether they might be infected, indicating that they should confirm the result with a formal test.

‘It could really revolutionize testing in the school system’

Such an app, if effective and publicly available, could have big impacts on the school reopening landscape, health experts say.

鈥淚t could really revolutionize testing in the school system,鈥� said Dr. Philip Chan, associate professor of medicine at Brown University and medical director for the Rhode Island Department of Health. 鈥淪hould something like this come to fruition, it would really be exciting.鈥�

Traditional screens for the virus, such as temperature checks and questionnaires, can identify positive cases only when individuals are already experiencing symptoms, explained Dr. Sara Johnson, co-director of the .

鈥淚f we鈥檙e relying purely on symptom-based screening, we鈥檙e missing a lot of people,鈥� she said.

Recently, some schools have rolled out rapid testing, said Chan, but even those tests require specimen collection, training, and have an associated cost per person. An effective diagnostic tool based on forced cough recordings through a smartphone would all but eliminate those barriers.

Still, both Johnson and Chan cautioned that it鈥檚 too early to count on any tech-based solutions.

鈥淢ore evidence is needed, more data is needed,鈥� Chan said. 鈥淭hey need to be validated, studied, especially in real-world settings.鈥�

Whether the artificial intelligence model is effective for young people remains an unanswered question 鈥� a key factor in the technology鈥檚 potential impacts for public schools. The MIT study indicates that more research will be required to find out.

鈥淸T]here are cultural and age differences in coughs,鈥� the paper notes in its conclusion. 鈥淸F]uture work could focus on tailoring the model to different age groups.鈥�

Bias concerns

Data privacy and bias also remain key worries. Algorithms used in other health-related contexts such as risk assessment have been shown to . And devices measuring blood oxygen levels, popular for coronavirus symptom regulation, can give .

Such problems in technology can stem from biased data, explained Harvard University computer science professor . If an artificial intelligence model is trained on a tainted sample, 鈥淸the] model is going to spit out exactly the inequity that鈥檚 in the dataset because it only learns from the data,鈥� she told The 74.

Additionally, if the dataset does not represent a diverse sample of people, the model can provide inaccurate results for people whose gender and racial identities are underrepresented in the trials. 鈥淚f you have a model that鈥檚 been trained on one subgroup of people,鈥� explained Doshi-Velez, 鈥渢hen there鈥檚 no telling what it will do on a different subgroup.鈥�

The Harvard professor鈥檚 primary question with the MIT research is whether its dataset includes a diverse set of individuals. 鈥淲hat demographics did you collect and what were the performances across the demographics?鈥� she wonders.

But while Doshi-Velez considers the idea 鈥渃lever鈥� despite her concerns, NYU research scientist harshly critiqued the study over Twitter, taking aim at the 鈥渟cant鈥� 5,320 samples collected and chalking the results up to 鈥渂iological essentialist AI hype that will not work.鈥�

Though the MIT study鈥檚 co-authors declined to comment on Whitaker鈥檚 critique, their paper says that the team is working with several hospitals around the world to collect a larger, more diverse set of cough recordings, which will help to strengthen the model鈥檚 accuracy 鈥� and hopefully minimize potential bias.

While this latest cough model represents a breakthrough in the COVID-19 screening landscape, the idea of using vocal sound to diagnose illness is not new. For years, research groups already had been training algorithms on cell phone recordings of coughs to accurately diagnose conditions such as pneumonia and asthma. Embedded in the sound of a cough, these models can pick out a number of factors (or 鈥渂iomarkers,鈥� as researchers refer to them) including gender, emotional state, mother tongue, vocal cord strength, respiratory performance, and muscular degradation.

School staff divided on app

In addition to fears of a faulty algorithm, Maya Chavez, a high school teacher in Providence, Rhode Island, worries that a pre-screening app might give school leaders a false sense of security if implemented before conclusive data on the artificial intelligence model鈥檚 effectiveness emerges. She would not want to see an app operate as a stand-in for more accurate diagnostic measures such as PCR, or nasal swab, testing, which experts consider the 鈥溾�� for COVID-19 detection.

鈥淲e need to be very careful about lower-quality solutions being presented for high (case) rate areas,鈥� said Chavez, who is keenly aware that in her district, which serves 91 percent students of color, systemic inequities such as old buildings and low academic standards persist dangerously. Countywide in Providence, .

However, if the technology does prove effective, other staff in Chavez鈥檚 district see the potential benefit of incorporating the additional screening measure. Melanea Lopez-Vallejo, a high school guidance counselor and ESL teacher in Providence who herself got infected in school from a student waiting on test results, thinks a phone-based screening system might have saved her from getting sick.

鈥淚t would work perfectly for us,鈥� Lopez-Vallejo said of the prospect of a smartphone app. Thanks in part to , Providence schools have mostly remained open for hybrid or full in-person learning, even as virus cases have surged. The state set up a robust testing system, but those measures cover students and staff experiencing COVID-19 symptoms or exposure, not those who may be asymptomatic.

But while educators and public health experts mull the possibility of identifying COVID-19 through algorithmic means, the app-based idea might not be the most unlikely screening method. Researchers are also exploring the possibility that .

Still, while Johnson, Johns Hopkins Consortium, welcomes innovation in helping to combat the virus, she cautions against relying too heavily on solutions that might leave under-resourced communities behind.

鈥淭here is still going to be a segment of the population that doesn鈥檛 have access to apps, they don鈥檛 have access to smartphones, or they run out of [data],鈥� she said. 鈥淚t鈥檚 going to be incredibly important also to think about strategies that work in less well-resourced settings and make sure that those kids don鈥檛 get left behind.鈥�

If the MIT research team can mitigate these concerns, they see a huge upside to using artificial intelligence to screen for infection 鈥� both for schools and for society writ large.

鈥淧andemics could be a thing of the past if pre-screening tools are always on in the background and constantly improved,鈥� their paper concludes.

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