Seok-Bum Ko (left) and Yi Wang whose software “learns” to spot disease...
Seok-Bum Ko (left) and Yi Wang whose software “learns” to spot disease signs from medical images.
Source: Dave Stobbe for USask

Deep learning system automatically detects diseases

University of Saskatchewan PhD student Yi Wang developed a software that can get higher image quality. It improves current computer-aided diagnosis (CADx) technology, which assists doctors to detect diseases from medical imaging scans such as ultrasound, computer tomography (CT) and retinal fundus imaging, which captures photos of the back of the eye.

Wang’s software makes diagnosis faster — it takes less than 30 seconds and it is around 10 times faster than current ones. “Our software will help medical staff reduce the time they take to interpret medical images, so that they can provide better patient care,” said Seok-Bum Ko, an electrical and computer engineering professor and Wang’s supervisor. “Radiologists and doctors can use their saved time more efficiently for other important tasks.”

Wang has tested his software on detecting abnormal retinal blood vessels in the eye — a symptom common to diabetes or heart disease — and was 97 per cent accurate at identifying abnormal vessels that needed further diagnosis.

Detecting blood vessels from retinal fundus imaging is often difficult. The images may end up blurred, so the vessels may be difficult to identify. Also, doctors usually have to mark blood vessel patterns manually on the image to determine whether vessels are broken — a time consuming process.

Wang’s software uses a state-of-the-art system that helps improve image classification and quality. “Deep learning relies on software algorithms that make the software automatically learn and analyze image patterns,” said Wang. “The idea is that the more images the software ‘reads,’ the better and more accurate it becomes at distinguishing healthy vessels from broken ones, so we may say it progressively ‘learns.’ This idea is at the core of all studies on artificial intelligence.”

To prove that his software works, Wang tested it on more than 130 images taken from a public database where diagnoses were already available, so that he could compare systems. His software is proved to be two per cent more accurate than commercial counterparts. “Our software is a good tool to complement radiologists’ and doctors’ expertise, not to substitute it,” said Ko. “There is a concern that this type of new ‘intelligent’ technologies will replace humans, like in science fiction. That is not the case, because we will always need people to make machines work.”

Wang and Ko, who have been awarded funding from the federal agency NSERC, are already teaching the software to detect lung and breast cancer from CT and ultrasound images respectively, with very positive results. “We are very excited about our detection system, and we are sure it will also make a change in medical teaching,” said Ko.

Subscribe to our newsletter

Related articles

Deep learning-based image segmentation

Deep learning-based image segmentation

Scientists have presented a new method for configuring self-learning algorithms for a large number of different imaging datasets – without the need for specialist knowledge or very significant computing power.

How AI can improve medical imaging

How AI can improve medical imaging

AI offers not only the possibility of better detection of a tumor, a skin lesion or some other indication but also can improve accuracy and efficiency for radiologists.

Deep learning platform accurately diagnoses dystonia

Deep learning platform accurately diagnoses dystonia

Researchers have developed a unique diagnostic tool that can detect dystonia from MRI scans, the first technology of its kind to provide an objective diagnosis of the disorder.

Dentistry: AI helps localize the mandibular canals

Dentistry: AI helps localize the mandibular canals

Researchers have developed a new model that accurately and automatically shows the exact location of mandibular canals.

AI outperform doctors: Experts express concerns

AI outperform doctors: Experts express concerns

Many studies claiming that AI is as good as (or better than) human experts at interpreting medical images are of poor quality and are arguably exaggerated, warn researchers in The BMJ.

ConvPath software uses AI to identify cancer cells

ConvPath software uses AI to identify cancer cells

A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.

AI rivals radiologists at detecting brain hemorrhages

AI rivals radiologists at detecting brain hemorrhages

An algorithm did better than experts radiologists at finding tiny brain hemorrhages in head scans — an advance that one day may help doctors treat patients with strokes.

How AI is already helping physicians save lives

How AI is already helping physicians save lives

With RAPID AI, the physicians now can get find parts of the brain that are not currently getting enough blood flow within minutes.

“CheXNeXt” outperformed radiologists in evaluating chest X-rays

“CheXNeXt” outperformed radiologists in evaluating chest X-rays

In a matter of seconds, a new algorithm read chest X-rays for 14 pathologies, performing as well as radiologists in most cases, a Stanford-led study says.

Popular articles

Subscribe to Newsletter