AI Case Study

Oregon Health and Science University trained deep learning models on used machine learning to diagnose the leading childhood blindness disease with 91% accuracy bettering the 82% of opthalmologists

Oregon Health and Science University trained deep learning models to better predict the leading cause of childhood blindness, retinopathy of prematurity (ROP). The models were first trained on 5,000 eye images to identify retinal vessels. The model was then trained to distinguish between diseased and healthy with an accuracy of 92% compared to 82% for ophthalmologists. The potential is significant as there are a shortage of ophthalmologists even in the US. The research is now being trialled in India.

Industry

Public And Social Sector

Education And Academia

Project Overview

The algorithms use deep learning. "They first trained the algorithm to identify retinal vessels in more than 5,000 pictures taken during infant visits to an ophthalmologist. Next, they trained it to differentiate between healthy and diseased vessels. Afterward, they compared the algorithm's accuracy with that of trained experts who viewed the same images and discovered it performed better than most of the expert physicians."

"They first trained the algorithm to identify retinal vessels in more than 5,000 pictures taken during infant visits to an ophthalmologist. Next, they trained it to differentiate between healthy and diseased vessels. Afterward, they compared the algorithm's accuracy with that of trained experts who viewed the same images and discovered it performed better than most of the expert physicians."

Reported Results

"The algorithm was shown sample images of eye scans and correctly diagnosed patients with ROP 91 percent of the time. On the other hand, a team of eight physicians with ROP expertise who examined the same images had an average accuracy rate of 82."

Technology

"They first trained the algorithm to identify retinal vessels in more than 5,000 pictures taken during infant visits to an ophthalmologist. Next, they trained it to differentiate between healthy and diseased vessels."

Function

R And D

Core Research And Development

Background

"Retinopathy of prematurity is caused by abnormal blood vessel growth near the retina, the light-sensitive portion in the back of an eye. The condition is common in premature babies and is the leading cause of childhood blindness globally.

The National Eye Institute of the National Institutes of Health reports that up to 16,000 U.S. babies experience retinopathy of prematurity to some degree, but only up to 600 become legally blind each year as a result. The condition is becoming more common as medical care for premature babies improves.

The disease is diagnosed by visually inspecting a baby's eye. Physicians typically use a magnifying device that shines light into a baby's dilated eye, but that approach can lead to variable and subjective diagnoses."

There is a shortage of ophthalmologists that are trained and willing to diagnose the disease even in the US. This results in many children going undiagnosed around the world.

Benefits

Data

Data included "...retinal vessels in more than 5,000 pictures taken during infant visits to an ophthalmologist." These were labelled to identify healthy versus diseased vessels.