Diagnosing Novel CoronaVirus COVID-19 within few seconds!!!! Possible?

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Let’s move on to today's topic: Diagnosing Novel CoronaVirus COVID-19 within few seconds!!!! Possible?

In recent times the coronavirus cases are increasing rapidly no-no exponentially. The typical method for diagnosing coronavirus in humans is such an unpleasant one. Now the famous cricketer Ashwin Ravichandran also shared his discomfort like pain and irritation while taking the test specimen. One thing we want to note from the typical method is more accurate method for diagnosing coronavirus on humans. Here, I mentioned Reverse Transcription-Polymerase Chain Reaction (RT-PCR ) swab tests as a typical test. The RT-PCR nasopharyngeal and oropharyngeal tests are more widely used and familiar ones. It involves a process of sticking an approximately 15-centimeter swab deep inside the nose and throat. It takes more than a day to analyze the samples and to declare the results of the respective samples.

To overcome this problem the world countries are trying to find the non-invasive diagnostic device for coronavirus and some of the leading countries have published research articles on a non-invasive breath analyzer called a breathalyzer.

Now, The ACS Nano has developed a prototype device that non-invasively detects COVID-19 in the exhaled breath of infected patients and it is very much similar to the breath analyzer for alcohol detection.

The researchers published their paper in the name of "Multiplexed Nanomaterial based sensor array for detection of COVID-19 in exhaled breath". (link provided at the end of the page)

Let's come back to the breathalyzer the researchers say, In the proposed method we constructed a sensor with the eight different gold nanoparticles are linked to the molecules that are sensitive to the various Volatile Organic Compounds(VOCs) exhaled from our breath. At the time of exhalation, the VOCs interact with our sensor and it causes changes in the electrical resistance. The main advantage of this sensor is flexibility. So, it can be trained to identify a wide range of chemical patterns. It can be used for various applications by using pattern recognition and machine learning algorithms.

They used a machine-learning algorithm here to detect COVID-19 in patients. Their team tested the accuracy of the device in the test set, The device showed 76 percent in distinguishing COVID-19 cases from controls and 95 percent accuracy in discriminating COVID-19 cases from lung infections. It also distinguishes between Sick and Recovered COVID-19 patients with 88 percent accuracy.

The below image shows the overall process of a breath analyzer:

At last, come to our question.

Yes, We can diagnose coronavirus on our body within a few seconds by using this breath analyzer (breathalyzer). But before the commercialization, the prototype must require more and more training sets. By feeding more training data sets and test data sets we can reduce the error rate and increase the accuracy of the output in the algorithm.

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SOME INTERESTING FACTS:

Why they used machine learning in their prototype?

It is a type of Artificial Intelligence (AI). It provide systems the ability to improve automatically through experience.

ML Components:

1. Algorithm - a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer. Depending on the algorithm, the accuracy or speed of getting the results can be different.

2. Data set - Machine learning systems are trained on special collections of samples called datasets. The samples can include numbers, images, texts or any other kind of data.

• Training data set- a subset to train a model.

• Testing data set - a subset to test the trained model.

3. Features- They are individual independent variables that act as the Input in your system. prediction models use features to make predictions.

Reference:

“Multiplexed Nanomaterial-Based Sensor Array for Detection of COVID-19 in Exhaled Breath” by Benjie Shan, Yoav Y Broza, Wenjuan Li, Yong Wang, Sihan Wu, Zhengzheng Liu, Jiong Wang, Shuyu Gui, Lin Wang, Zhihong Zhang, Wei Liu, Shoubing Zhou, Wei Jin, Qianyu Zhang, Dandan Hu, Lin Lin, Qiujun Zhang, Wenyu Li, Jinquan Wang, Hu Liu, Yueyin Pan and Hossam Haick, 18 August 2020, ACS Nano.

https://pubs.acs.org/doi/10.1021/acsnano.0c05657

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