Fujitsu Cough in a Box

Fujitsu’s Cough in a Box is an app funded by the UK’s Department of Health and Social Care to collect and analyse audio recordings of COVID-19 symptoms. 

Risks and harms

Research by The Alan Turing Institute and Royal Statistical Society, commissioned by the UK Health Security Agency, discovered that cough-analysing algorithms do a poor job of diagnosing COVID-19. The team found that even the most accurate cough-detecting model performed worse than a model based on user-reported systems and demographic data, such as age and gender. 

One such model was Fujitsu’s Cough in a Box, an app funded to the tune of GBP 100,000 in 2021 by the UK’s Department of Health and Social Care to collect and analyse audio recordings of COVID-19 symptoms. This was despite a Massachusetts Institute of Technology (MIT) team finding that, when new cough sounds were introduced, the algorithm accurately identified 98.5% of coughs from people who were confirmed to have COVID-19, including 100% of coughs from people with no symptoms.

The researchers had used technology that aimed to identify a signature noise, known as a biomarker, in a person’s cough that could be linked to a positive COVID-19 PCR test result. Their findings led to efforts to build an algorithmic-powered app to provide people with a cheap and easy method to test for COVID-19.

Research, advocacy

• Coppock H. et al (2022). Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

• Pigole D. et al (2022). Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19

• Budd J. et al (2022). A large-scale and PCR-referenced vocal audio dataset for COVID-19