A group of international researchers from Australia, China and the United States has developed an artificial intelligence system capable of detecting and monitoring retinal diseases on a larger scale. An innovation they called the CARE (AI Comprehensive Retina Expert) system.
According to the researchers, this system was developed using fundus photography, which involves taking pictures of the inside of the eye through the pupil to detect retinal diseases, combined with a machine learning system, trained with data from case studies. real diseases of the retina. This was then externally tested using fundus photographs collected in clinical settings where the model is most likely to be adopted.
“The CARE system was trained to identify the 14 most common retinal abnormalities using 207,228 color fundus photographs from 16 clinical facilities in Asia, Africa, North America, and Europe, with different disease distributions,” said Zongyuan Ge, professor. Associate in the Department of Electricity and Computer Systems Engineering at Monash University in Australia, responsible for large-scale research.
“The CARE system was internally validated using 21,867 photographs and externally tested using 18,136 photographs collected prospectively in 35 real-world settings in China, including eight private hospitals, six community hospitals, and 21 physical examination centers. “
The system’s performance was then compared to that of 16 ophthalmologists and tested using data sets featuring non-Chinese ethnicities and camera types that had not been used before. Based on these tests, the performance of the CARE system is similar to that achieved by professional ophthalmologists. The system also maintained strong identification performance when tested with non-Chinese data sets.
“These results indicate that the system is accurate compared to professional results and could allow for further testing on a larger scale,” he says. The researchers hope that the CARE system will be commercially available in China, before launching it internationally. They plan to build a database of real-world detection images, which can be deployed in clinical settings, in order to better diagnose retinal diseases.
“I hope this work allows us to continue to see technological advancements in this area,” said Amitha Domalpally, director of the Center for Diagnostic Imaging at the American University of Wisconsin-Madison.
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