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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 the disease. The prevalence rate of diabetic retinopathy ranges between 24.7% and 37.5%. With an aging population, the incidence of age-related macular degeneration is also on the rise. However, the current challenges in ophthalmology include a shortage of trained ophthalmologists, limited medical resources in primary hospitals, and inefficient diagnostic methods, all of which hinder effective eye care.
To address these issues, the long-range vision intelligent viewing platform, developed through the integration of artificial intelligence (AI) and ophthalmology, offers accurate diagnosis, quantitative analysis, and timely screening of fundus images. This AI-powered system greatly assists doctors in making faster and more reliable diagnoses, especially in regions where ophthalmologists are scarce. Through efficient telemedicine systems, primary hospitals can gradually reach the diagnostic level of larger institutions.
Ophthalmic grading treatment is becoming increasingly feasible. The growing concern around eye health has led to a rise in high myopia among young people. 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 is crucial for managing conditions like age-related eye diseases and chronic illnesses.
Auror Ophthalmic's subsidiary, Hunan Vision Technology Co., Ltd., plays a central role in its internet-based strategy. It provides eye health examinations, optometry services, eye health products, personal eye doctors, remote diagnosis, and referral services, creating an "Internet +" model that blends online and offline support.
The current state of China’s ophthalmological medical service industry is fragmented and regionally concentrated. However, advancements in telemedicine, smart devices, and big data are enabling a more structured approach to graded diagnosis and treatment. Companies like Intel Joint Eyes, Extreme Vision, and Jinhong Technology have introduced AI-based solutions for disease recognition, building long-range remote intelligent reading platforms to enhance the accuracy and efficiency of diabetic retinopathy (DR) and age-related macular degeneration (AMD) screenings.
The neighboring remote intelligent reading platform supports both graded diagnosis and comprehensive eye health management. Xu Ming, General Manager of Hunan AELINK Internet Technology Co., Ltd., emphasized that this platform helps centralize resources to meet the decentralized needs of local healthcare facilities, where ophthalmologists are in short supply.
By collecting data from community health centers, optometry clinics, county hospitals, and optical shops, the platform performs intelligent readings, sending abnormal images to specialists for further review. All fundus images contribute to continuous learning and improvement of the system. The results are then shared with patients, and referrals are made as needed.
Sun Tao, Deputy General Manager and CTO of Hunan Aurora Connect Technology Co., Ltd., noted that the remote intelligent interpretation platform is particularly beneficial at the grassroots level due to the lack of skilled ophthalmologists. By deploying equipment in community centers, initial screenings can be done by AI, with questionable images sent back for expert evaluation.
Currently, nearly 20 community service centers and over 10 Aier Ophthalmology-built eye health centers are using the remote and 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 reading process takes just 10 seconds. The platform combines software—artificial intelligence and reading systems—with hardware, including a reading center and a team of doctors. It uses Convolutional Neural Networks (CNN), a machine learning technique that enables pattern recognition.
Initially, the platform focused on DR and AMD, as these conditions have the highest prevalence in ophthalmology and feature extraction is relatively straightforward. After a year and a half of training on 100,000 labeled fundus images, the system achieved a 93% accuracy rate in identifying DR and AMD, with ongoing efforts to expand to other eye diseases.
Aier Ophthalmic, Aurora Connect, Intel, and Jinhong Technology each play distinct roles: Aier provides access to hundreds of hospitals across the country for image data, Intel develops specialized chips for image interpretation, and Jinhong Technology produces fundus cameras and AI models.
All remote vision reading and image storage take place on a third-party cloud platform, while AI operations occur 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, described the platform as a cloud-to-end solution, enhancing AI capabilities with customized hardware and algorithm frameworks.
To ensure consistency, the platform maintains reading records so that primary readers can see final outcomes. Regardless of the type of fundus camera used, the platform can analyze images, making it highly scalable. The main challenge lies in deploying fundus cameras, but future use of handheld fundus inspection devices will significantly boost adoption. Zheng Zhuming, Chairman and Co-founder of Jin Hong Technology (Taiwan) Co., Ltd., emphasized that such innovations will accelerate the platform’s growth.