Researchers Builds A CARE System To Detect Retinal Diseases

CARE System Takes Photographs Of Interior Of Eye Through Pupil, To Detect And Monitor Retinal Diseases On A Large Scale More Accurately

Researchers Builds A CARE System To Detect Retinal Diseases

A group of international researchers has applied artificial intelligence (AI) technology to fundus photography, a process of taking photographs of the interior of the eye through the pupil, to detect and monitor retinal diseases on a large scale more accurately. The researchers trained a clinically applicable deep-learning system for fundus diseases using data derived from real-world case studies, and then externally tested the model using fundus photographs collected from clinical settings in China, said Associate Professor Zongyuan Ge from the Department of Electrical and Computer Systems Engineering at Monash University and the Monash Data Futures Institute. “The CARE system was trained to identify the 14 most common retinal abnormalities using 207,228 colour fundus photographs derived from 16 clinical settings across Asia, Africa, North America and Europe, with different disease distributions,” Associate Professor Ge said.

“CARE was internally validated using 21,867 photographs and externally tested using 18,136 photographs prospectively collected from 35 real-world settings across China, including eight tertiary hospitals, six community hospitals and 21 physical examination centres.” Professor Ge jointly developed the system with researchers from Sun Yat-sen University, Beijing Eaglevision Technology (Airdoc), University of Miami Miller School of Medicine, Beijing Tongren Eye Centre and Capital Medical University.

The researchers expect CARE to be adopted in medical settings across China and the Asia Pacific region. The performance of the system was compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. The system was found to be accurate when compared to the outcomes of professional ophthalmologists and could facilitate testing to be carried out on a larger scale, according to Associate Professor Ge.

“This research is a step in the right direction for medical and artificial intelligence research. I hope that through this work we can continue to see technological advancements in this space,” said Amitha Domalpally, Director of the University of Wisconsin-Madison Imaging Diagnostic Center. The research will also build out a database of screening images from real-world environments that can be rolled out in clinical settings to better diagnose retinal diseases.

This news was originally published at Hospital Health