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AI and ophthalmology combined with eye disease diagnosis takes only 10 seconds
There are approximately 110 million diabetic patients in China, and diabetic retinopathy is one of the most severe complications associated with diabetes. The prevalence rate of this condition ranges from 24.7% to 37.5%. As China's population ages, the incidence of age-related macular degeneration (AMD) is also on the rise. However, the current challenges in ophthalmology include a shortage of qualified ophthalmologists, limited medical resources in primary healthcare facilities, and inefficient diagnostic methods, all of which hinder effective eye disease management.
To address these issues, the long-range vision intelligent viewing platform, developed by integrating artificial intelligence (AI) with ophthalmology, offers accurate diagnosis, quantification, and timely screening of fundus images through intelligent analysis. This system not only supports doctors in making more informed decisions but also significantly improves diagnostic efficiency. In areas where ophthalmologists are scarce, telemedicine systems can help bridge the gap, allowing primary hospitals to gradually match the diagnostic capabilities of larger medical centers.
Ophthalmic grading treatment has now become more feasible. With the increasing severity of eye health issues, especially among younger generations who suffer from high myopia, early detection and intervention are crucial. According to data from the National Health and Family Planning Commission, over 50% of people do not receive regular eye exams, and more than 90% of patients seek treatment only after symptoms appear. Early intervention for conditions like AMD and other chronic eye diseases can make a significant difference.
Auror Ophthalmic’s subsidiary, Hunan Vision Technology Co., Ltd., plays a central role in its internet-based strategy, offering comprehensive eye health services such as eye exams, optometry, eye health products, personal eye doctors, remote diagnosis, and referral services. This has led to the creation of an integrated "Internet +" model for eye care, combining online and offline services.
Despite the current fragmented nature of China’s ophthalmological services, advancements in telemedicine, smart devices, and big data are paving the way for graded diagnosis and treatment. Companies like Intel, Extreme Vision, and Jinhong Technology have introduced AI-driven disease recognition solutions for the eyes, building remote intelligent reading platforms that enhance the accuracy and efficiency of screening for diabetic retinopathy (DR) and AMD.
The neighboring remote intelligent reading platform not only facilitates graded diagnosis but also supports comprehensive eye health management. Xu Ming, General Manager of Hunan AELINK Internet Technology Co., Ltd., emphasized that by centralizing resources, the platform helps meet the decentralized demand for eye care, particularly in areas lacking sufficient ophthalmologists.
Data from community health centers, optometry clinics, county hospitals, and optical stores is collected and analyzed using the remote intelligent platform. Abnormal images are sent for further review, and all fundus photos contribute to continuous learning and improvement. The results are then shared with patients, and referrals are made when necessary.
Sun Tao, Deputy General Manager and CTO of Hunan Aurora Connect Technology Co., Ltd., noted that the platform is especially valuable at the grassroots level due to the shortage of primary-level ophthalmologists. By equipping community health centers with AI-powered devices, initial screenings can be conducted automatically, and questionable images are reviewed by specialists.
Currently, nearly 20 community service centers and over 10 Aier Ophthalmology self-built eye health centers have begun using the remote intelligent reading platform. The goal is to expand to 50 centers by year-end, with major pilot cities including Changsha, Beijing, Chengdu, Tianjin, and Shenzhen.
The AI-based reading process takes just 10 seconds. The platform combines software (AI + reading system) with hardware (reading center + doctor team), utilizing Convolutional Neural Networks (CNN) for pattern recognition. DR and AMD were chosen as the first targets for AI screening due to their high prevalence and ease of feature extraction.
Through a year and a half of training on 100,000 labeled fundus images, the platform achieved a 93% accuracy rate for DR and AMD. Research is ongoing for other eye diseases. Different companies play distinct roles: Aier Ophthalmic provides the data and platform, Intel develops specialized chips, and Jinhong Technology supplies the fundus cameras and AI models.
All image storage and remote vision reading occur on a third-party cloud platform, while AI operations take place in a local data center, connected via a dedicated network. Zhang Xiaobo, Head of the Intel Data Center Medical and Life Sciences Group in Asia Pacific, highlighted that the platform represents a cloud-to-end solution, optimizing AI algorithms and improving calculation speed by 3–5 times, reducing image judgment time from one minute to under ten seconds.
Due to varying levels of expertise among readers, some cases require a second opinion. The platform maintains a reading history so primary readers can view the final results. Regardless of the type of fundus camera used, the platform can analyze the images, making it widely applicable.
The key challenge in promoting the platform lies in deploying fundus cameras. Zheng Zhuming, Chairman and Co-founder of Jin Hong Technology (Taiwan) Co., Ltd., stated that the future use of handheld fundus inspection devices will greatly accelerate the adoption of the platform.