Hopkins researchers chart a course for AI-aided diagnosis of degenerative eye conditions

Team from APL and Wilmer Eye Institute discover that machine diagnostics using deep learning can match the performance of human ophthalmologists

Artificial intelligence could improve how doctors identify and diagnose eye conditions such as age-related macular degeneration

A team of researchers from Johns Hopkins is leading the way in understanding how the advent of electronic medical records with large image databases and advances in artificial intelligence with deep learning can offer medical professionals new opportunities to dramatically improve disease diagnostics.
Researchers from the Johns Hopkins Applied Physics Laboratory and collaborators at the Johns Hopkins School of Medicine have developed image analysis and machine learning tools to detect age-related macular degeneration, or AMD, a condition that causes lesions on the part of the eye called the retina. When not detected early, the vision loss caused by the lesions is often irreversible. It is the leading cause of blindness in individuals over the age of 50.

“WE WERE ABLE TO SHOW THAT MACHINES CAN DO AS WELL AS HUMANS FOR DIAGNOSING AMD.”

Philippe Burlina
Co-principal investigator

The APL team partnered with researchers at the Johns Hopkins Wilmer Eye Institute on ways to automate AMD diagnosis, discovering that machine diagnostics using deep learning can match the performance of human ophthalmologists. The findings are published in JAMA Ophthalmology.
“We’ve been able to show the feasibility of automated fine-grained classification of AMD severity that only highly trained ophthalmologists can achieve,” said APL’s Philippe Burlina, a co-principal investigator for the project. “These techniques have the potential to provide individuals with automated grading of images to identify AMD or monitor those individuals with earlier stages of AMD for the onset of the more advanced stages when prompt treatment may be indicated to reduce the risk of blindness.”

The team has also expanded its inquiry to characterize layers of the retina during optical coherence tomography, or OCT, a noninvasive imaging technique that provides high-resolution, cross-sectional images of the retina, retinal nerve fiber layer, and optic nerve head. Such techniques can be used to diagnose other retinal diseases such as diabetic retinopathy and have the potential to help characterize vascular and neurodegenerative pathologies……

Read more: https://hub.jhu.edu/2018/07/31/macular-degeneration-diagnosis-ai-machine-learning/
Source: HUB
 

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