Massive AI Breakthrough Could Mean a New COVID-19 Pre-Screening Method in the Cards
By: Natalie Mclaren
Tags: Technology, Artificial Intelligence, Science, Coronavirus, Covid 19

The Massachusetts Institute of Technology (MIT) has recently released information about a potential game-changer in screening for COVID-19 asymptomatic patients.

So far during this pandemic, scientists have identified that there are individuals who show symptoms, and those who are infected but show no symptoms at all. These pose a substantial danger given they feel no need to stay at home, and rightly so, and thus potentially contaminating whoever they are in contact with.

Researches at MIT, however, have developed an artificial intelligence model that can detect the virus in asymptomatic individuals. After being fed with new recordings, the model correctly identified 98.5% of coughs from people who had tested positive, including 100% of coughs from asymptomatic people who showed no symptoms but had tested positive for the virus.

But how does it work?

Some background on AI models without going too deep — an AI model is typically fed a large amount of the same type of data you want it to look out for.

Let’s say we’re training a model to differentiate between manager signatures. We would give our cognitive model (which may be looking out for a range of parameters such as colour, length, thickness, etc of a signature) a large volume of signature images. How large is large? As many signatures, as you can acquire (with permission from the managers of course) — think hundreds if not thousands. The more data your model has to work with, the stronger it will become at differentiating between those signatures.

Each signature is fed to the model along with a label containing the name of the manager whose signature it belongs to. Once you’re happy it has been trained with all of the available signatures, you would feed it completely new signatures that were not part of the training data. This is really important! You don’t want to test your model on a data set you have trained it with. It is also just as important to feed your model with images that don’t contain any signatures from those managers whose signatures you wish to identify in the future. Why? Because it's important for it to also learn to correctly say if none of the signatures it was expecting to find has been found.

Providing the training has gone well and it was given a large enough sample of data, the model will correctly differentiate between signatures.

This, of course, is a slightly different, and for sure oversimplistic, example compared to how the MIT model works. But you hopefully understand the concept behind AI models. Train them with enough of the same dataset type, test them with new data.

Back to COVID

The researchers at MIT tested their AI model on thousands of cough and dialog samples — allowing for a very rich training dataset.

Which parameters exactly was this model looking out for? This particular AI model has been around for some time — it was initially intended to detect signs of Alzheimer's through forced-coughs given the effects that the disease is known to have on vocal cords. Vocal cord strength, along with sentiment, lung and respiratory performance, and muscular degradation were effective in detecting the disease.

As the pandemic unfolded, researchers wondered whether the same model could be used to identify COVID-19 infected individuals given the effects the virus has on our muscles (with some individuals reporting temporary muscle impairment).

Researchers found that without modifying the model too much, it was able to correctly identify asymptomatic COVID-19 patients based on the same biomarkers (vocal cord strength, along with sentiment, lung and respiratory performance, and muscular degradation) that were used to detect Alzheimer’s in individuals.

What’s next?

Researchers are currently looking at developing a user-friendly app based on the model. If approved by officials, they hope it could act as a pre-screening app for the virus. We may be looking at a future in which a day out is dependent on results after coughing into your phone!

What a fantastic way to put our technological advancements to use.