Tuesday July 23, 2019

Researchers Develop AI-enabled Tool to Detect Heart Attacks

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not

0
//
A commonly used drug for treating osteoporosis or bone pain may also help reduce the risk of death by cardiovascular, heart attack and stroke, according to a study.
Heart Attack risk can be decreased by drug of osteoporosis. Pixabay

Researchers have developed an Artificial Intelligence-enabled tool which uses Machine Learning (ML) algorithms that will soon play a critical role in predicting heart attacks and other cardiac issues.

The Coronary Computed Tomography Arteriography (CCTA) gives highly detailed images of the heart vessels and is a promising tool for refining risk assessment, said researchers in the study published in the journal Radiology.

While earlier tools like the Coronary Artery Disease Reporting and Data System (CAD-RADS) emphasise on stenoses or blockages and narrowing in the coronary arteries, CCTA shows more than just stenoses.

“While CAD-RADS is an important and useful development in the management of cardiac patients, its focus on stenoses may leave out important information about the arteries,” said study lead author Kevin M. Johnson, Associate Professor at the Yale University.

Heart
The study compared people aged 41-50 years and 40 or younger heart attack survivors and found that among patients who suffer a heart attack at a young age overall is 40 or younger. VOA

The ML algorithm is able to pull out patterns in the data and predict that patients with certain patterns are more likely to have an adverse event like a heart attack than patients with other patterns.

For the study, the research team compared the ML approach with CAD-RADS and other vessel scoring systems in nearly 7,000 patients. They followed the patients for an average of nine years after CCTA.

Also Read: Indians Seek Personalized Customer Experiences The Most, Says a New Study by Adobe

It was found that compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not.

“The risk estimate that you get from doing the Machine Learning version of the model is more accurate than the risk estimate you’re going to get if you rely on CAD-RADS,” Johnson said. (IANS)

Next Story

Researchers Develop AI-driven System to Curb ‘Deepfake’ Videos

Roy-Chowdhury, however, thinks we still have a long way to go before automated tools can detect “deepfake” videos in the wild

0
Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

At a time when “deepfake” videos become a new threat to users’ privacy, a team of Indian-origin researchers has developed Artificial Intelligence (AI)-driven deep neural network that can identify manipulated images at the pixel level with high precision.

Realistic videos that map the facial expressions of one person onto those of another — known as “deepfakes”, present a formidable political weapon in the hands of nation-state bad actors.

Led by Amit Roy-Chowdhury, professor of electrical and computer engineering at the University of California, Riverside, the team is currently working on still images but this can help them detect “deepfake” videos.

“We trained the system to distinguish between manipulated and nonmanipulated images and now if you give it a new image, it is able to provide a probability that that image is manipulated or not, and to localize the region of the image where the manipulation occurred,” said Roy-Chowdhury.

A deep neural network is what AI researchers call computer systems that have been trained to do specific tasks, in this case, recognize altered images.

These networks are organized in connected layers; “architecture” refers to the number of layers and structure of the connections between them.

While this might fool the naked eye, when examined pixel by pixel, the boundaries of the inserted object are different.

For example, they are often smoother than the natural objects.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

By detecting boundaries of inserted and removed objects, a computer should be able to identify altered images.

The researchers tested the neural network with a set of images it had never seen before, and it detected the altered ones most of the time. It even spotted the manipulated region.

“If you can understand the characteristics in a still image, in a video it’s basically just putting still images together one after another,” explained Roy-Chowdhury in a paper published in the journal IEEE Transactions on Image Processing.

“The more fundamental challenge is probably figuring out whether a frame in a video is manipulated or not”.

Also Read: TikTok Testing New Features Inspired by Instagram

Even a single manipulated frame would raise a red flag.

Roy-Chowdhury, however, thinks we still have a long way to go before automated tools can detect “deepfake” videos in the wild.

“This is kind of a cat and mouse game. This whole area of cybersecurity is in some ways trying to find better defense mechanisms, but then the attacker also finds better mechanisms.” (IANS)