Title: Application of Artificial Intelligence System for Screening Multiple Fundus Diseases in Chinese Primary Healthcare Settings: A Real-World, Multicentre and Cross-Sectional Study of 4795 Cases

Authors: Chufeng Gu, Yujie Wang, Yan Jiang, Feiping Xu, Shasha Wang, Rui Liu, Wen Yuan, Nurbiyimu Abudureyimu, et al., Qinghua Qiu, Zhi Zheng, Deng Liu, and Jili Chen

Publication Source: British Journal of Ophthalmology (2024)

Disparities in healthcare access between urban centers and remote regions present a significant challenge for early eye disease detection. Deploying artificial intelligence in primary care clinics offers a scalable solution to standardizing diagnostic quality across geographically and economically diverse populations.

A multi-center, real-world study published in the British Journal of Ophthalmology evaluated the Airdoc Retinal AI System (ARAS) across six primary healthcare centers in Shanghai (a high-income urban metropolis) and Xinjiang (a lower-income western region).

What Was the Study Testing?

The study evaluated 4,795 participants to assess the diagnostic efficacy of ARAS in primary care settings and analyze regional differences in fundus disease prevalence.

The system combines a dual-module architecture:

• Precision Localization: Uses a YOLO-v3 object detection network to identify key anatomical landmarks (the macula and optic disc).
• Multi-Task Classification: Employs an EfficientNet-B3 network to classify 14 distinct fundus conditions simultaneously.
• Comparative Regional Analysis: Compares disease patterns between urban Shanghai and rural Xinjiang to optimize local healthcare resource allocation.

Key Findings: Diagnostic Accuracy & Regional Patterns

The study confirmed high screening accuracy while uncovering distinct regional health profiles:

1. Diagnostic Efficacy in Primary Care

• Referable Diabetic Retinopathy (RDR): Achieved an overall accuracy of 0.93, with a sensitivity of 0.87 and specificity of 0.93.
• Glaucomatous Optic Neuropathy (GON): Delivered an accuracy of 0.93, sensitivity of 0.95, and specificity of 0.92.
• Pathological Myopia (PM): Recorded an accuracy of 0.93, sensitivity of 0.85, and specificity of 0.94.

2. Regional Disease Spectrum Variations

• Shanghai (Urban): Displayed a significantly higher prevalence of pathological myopia and retinal drusen, reflecting lifestyle factors associated with dense urban environments.
• Xinjiang (Rural/Developing): Exhibited higher rates of referable diabetic retinopathy, macular edema, and retinal vein occlusion among middle-aged and older adults, pointing to a need for stronger chronic disease management in remote areas.

What Does This Mean for Healthcare & Screening?

The study highlights how AI screening can adapt to different regional healthcare demands. In urban settings, AI systems help manage high volumes of myopia and age-related macular conditions. In remote or resource-limited regions, they serve as a critical frontline defense for detecting unmanaged diabetic complications and glaucoma before irreversible vision loss occurs.

By automating early disease detection in primary care, health systems can streamline referral workflows, reduce patient travel burdens, and deploy medical resources where they are needed most.

The Bottom Line

AI-powered fundus screening bridges regional healthcare gaps, delivering consistent, high-accuracy diagnostic support in both urban hospitals and rural community clinics.

Schedule a visit with an eye care specialist to explore how modern imaging and AI tools can help safeguard your vision.

Read the full study in the British Journal of Ophthalmology.