Tuesday September 25, 2018

This AI Tool May Accelerate Diagnosis Of Eye Diseases, Pneumonia

Besides eye diseases, the tool was able to differentiate between viral and bacterial childhood pneumonia with greater than 90 percent accuracy

0
//
33
ai
The researchers also used occlusion testing, which allowed them to show areas of greatest importance when reviewing the scan images. Pixabay
Republish
Reprint

A novel image-based diagnostic tool, developed using artificial intelligence (AI) and machine learning techniques, may potentially speed up diagnoses and treatment of patients with retinal diseases and pneumonia among children, researchers say.

The findings showed that the new tool uses big data and AI to not only recognize two of the most common retinal diseases — macular degeneration and diabetic macular edema — but also to rate their severity.

“Macular degeneration and diabetic macular edema are the two most common causes of irreversible blindness but are both very treatable if they are caught early,” said Kang Zhang, Professor at the University of California-San Diego.

ALSO READ: Is Your Child Avoiding Eye Contact? He May Be Anxious, Says New Study

“Deciding how and when to treat patients has historically been handled by a small community of specialists who require years of training and are concentrated mostly in urban areas.”

ai
It can also distinguish between bacterial and viral pneumonia in children based on chest x-ray images. IANS

“In contrast, our AI tool can be used anywhere in the world, especially in the rural areas. This is important in places like India, China, and Africa, where there are relatively fewer medical resources,” Zhang said.

For the study, published in the journal Cell, the team studied over 200,000 optical coherence tomography (OCT) images using a technique called transfer learning, where knowledge gained in solving one problem is stored by a computer and applied to different but related problems.

“Machine learning is often like a black box where we don’t know exactly what is happening,” Zhang said.

The researchers then compared the diagnoses from the computer with those from ophthalmologists who reviewed the scans.

ALSO READ: Chronic Diseases Raise Cancer and Mortality Risk

The results showed that the tool “could generate a decision on whether or not the patient should be referred for treatment within 30 seconds and with more than 95 percent accuracy”, Zhang said.

Besides eye diseases, the tool was able to differentiate between viral and bacterial childhood pneumonia with greater than 90 percent accuracy.

It can also discern between cancerous and non-cancerous lesions detected on scans, Zhang said. (IANS)

Click here for reuse options!
Copyright 2018 NewsGram

Next Story

AI Helps Find Source Of Radio Bursts 3 Billion Light Years Away From Earth

The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally.

0
Space, radio
'AI helps track down mysterious cosmic signals', Pixabay

Scientists say they have used artificial intelligence (AI) to discover 72 new fast radio bursts from a mysterious source about three billion light years away from Earth.

The initiative may advance the search to find signs of intelligent life in the universe, said researchers from the University of California, Berkeley in the US.

Fast radio bursts are bright pulses of radio emission mere milliseconds in duration, thought to originate from distant galaxies.

However, the source of these emissions is still unclear, according to the research published in The Astrophysical Journal.

Theories range from highly magnetised neutron stars blasted by gas streams from a nearby supermassive black hole, to suggestions that the burst properties are consistent with signatures of technology developed by an advanced civilization.

 

earth, radio
While most fast radio bursts are one-offs, the source here, FRB 121102, is unique in emitting repeated bursts. Wikimedia Commons

 

“This work is exciting not just because it helps us understand the dynamic behaviour of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms,” said Andrew Siemion from the University of California – Berkele.

 

Researchers are also applying the successful machine-learning algorithm to find new kinds of signals that could be coming from extraterrestrial civilisations.

While most fast radio bursts are one-offs, the source here, FRB 121102, is unique in emitting repeated bursts.

This behaviour has drawn the attention of many astronomers hoping to pin down the cause and the extreme physics involved in fast radio bursts.

The AI algorithms dredged up the radio signals from data were recorded over a five-hour period in 2017, by the Green Bank Telescope in West Virginia in the US.

Radio
The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally. (IANS)

An earlier analysis of the 400 terabytes of data employed standard computer algorithms to identify 21 bursts during that period.

“All were seen within one hour, suggesting that the source alternates between periods of quiescence and frenzied activity,” said Berkeley postdoctoral researcher Vishal Gajjar.

Also Read: HCL Launches AI Based ‘HCL Turbo’

The researchers developed the new, powerful machine-learning algorithm and reanalysed the 2017 data, finding an additional 72 bursts not detected originally.

This brings the total number of detected bursts from FRB 121102 to around 300 since it was discovered in 2012, researchers said. (IANS)