Monday June 24, 2019

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

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The researchers also used occlusion testing, which allowed them to show areas of greatest importance when reviewing the scan images. Pixabay

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.

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“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.”

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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.

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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)

Next Story

Google Street Powers Artificial Intelligence Tool to Supervise Road Infrastructure

The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images

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Artificial intelligence, road infrastructure
The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images. Pixabay

By tapping into Google Street View images, researchers have developed a new programme using artificial intelligence (AI) to monitor the stop and give way (yield) signs needing replacement or repair. The fully-automated system is based on AI-powered object detection to identify street signs in the freely available images.

Published in the journal of Computers, Environment and Urban Systems, the study shows the system detects signs with near 96 per cent accuracy, identifies their type with near 98 per cent accuracy and can record their precise geo-location from the 2D images.

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File:Opel Astra Google Street View. Wikimedia Commons

“The proof-of-concept model was trained to see ‘stop’ and ‘give way’ (yield) signs, but could be trained to identify many other inputs and was easily scalable for use by local governments and traffic authorities,” said the study lead author Andrew Campbell from RMIT University in Australia.

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Municipal authorities spend a large amount of time and money monitoring and recording the geo-location of traffic infrastructure manually, a task which also exposes workers to unnecessary traffic risks. “By using free and open source tools, we’ve developed a fully automated system for doing that job, and doing it more accurately,” Campbell said. (IANS)