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And now an AI-based software tool for automated diagnosis of COVID-19 lung infection

And now an AI-based software tool for automated diagnosis of COVID-19 lung infection
outlookindia.com
1970-01-01T05:30:00+0530
Bengaluru, Feb 17 (PTI) A new software tool that reveals

the severity of lung infections in COVID-19 patients has been

developed by researchers from the Indian Institute of Science,

in collaboration with Oslo University Hospital and University

of Adger in Norway.

It has been described in a recent study published in the

journal "IEEE Transactions on Neural Networks and Learning

Systems", Bengaluru-based IISc said in a statement.

COVID-19 can cause severe damage to the respiratory

systems, especially the lung tissues. Image-based methods such

as X-ray or CT scans can prove helpful in determining how bad

the infection is, the statement noted.

The software tool, developed by the Departments of

Computational and Data Science and Instrumentation and Applied

Physics at IISc, called AnamNet, can ''read'' the chest CT scans

of COVID-19 patients, and, using a special kind of neural

network, estimate how much damage has been caused in the

lungs, by searching for specific abnormal features, it said.

"Such a tool can provide automated assistance to doctors

and therefore help in faster diagnosis and better management

of COVID-19", according to IISc.

AnamNet employs deep learning and other image processing

techniques, which have now become integral to biomedical

research and applications. The software can identify infected

areas in a chest CT scan with a high degree of accuracy, it

said.

The researchers trained AnamNet to look for abnormalities

and classify areas of the lung scan as either infected or not

infected - this is called segmentation.

The tool can judge the severity of the disease by

comparing the extent of infected area with healthy area.

"It basically extracts features from the chest CT images

and projects them onto a non-linear space (a mathematical

representation), and then recreates the (segmented) image from

this representation. This is called anamorphic image

processing," explains Naveen Paluru, first author and PhD

student in the lab of Phaneendra Yalavarthy, Associate

Professor at CDS.

The study also compared AnamNets performance with other

state-of-the-art software tools which perform similar tasks.

It not only matched its peers in its accuracy, but also

performed just as well using fewer parameters. The neural

network was also computationally less complex, which allowed

the researchers to train it much faster to detect anomalies,

it was stated.

Another significant advantage of AnamNet is that the

software is lightweight with a small memory footprint. This

has enabled the team to develop an app called CovSeg that can

be run on a mobile phone and hence potentially be used by

healthcare professionals.

"We felt the need for a lightweight framework that could

be deployed as a point-of-care diagnostic device on

smartphones or a Raspberry Pi," says Paluru.

He adds that this feature is missing from currently

available state-of-the-art technologies such as UNet, which

requires specialised hardware.

According to the authors, AnamNet holds promise beyond

merely identifying lung infections in COVID-19 patients.

"We are currently focusing on making our software more

robust to handle COVID-19 scans, but we are also looking to

diversify to other common lung diseases like pneumonia,

fibrosis and even lung cancer in the near future, Yalavarthy

says.

He suggests that with some changes to the present design,

the software could even be used to read brain scans.

The software tool is freely available to the public, IISc

added. PTI RS APR

ADMINISTRATOR

APR

ADMINISTRATOR


Disclaimer :- This story has not been edited by Outlook staff and is auto-generated from news agency feeds. Source: PTI

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