Zhang Juzhao, Lin Senlin, Cheng Tianhao, Xu Yi, Lu Lina, He Jiangnan, Yu Tao, Peng Yajun, Zhang Yuejie, Zou Haidong, Ma Yingyan
Shanghai Eye Disease Prevention & Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
National Clinical Research Center for Eye Disease, Shanghai, China.
NPJ Digit Med. 2024 Apr 30;7(1):108. doi: 10.1038/s41746-024-01109-5.
Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.
视力障碍和失明是全球主要的公共卫生问题。借助人工智能（AI）进行有效的眼病筛查是一种很有前景的应对措施，尽管它面临着一些实际限制，如社区筛查中图像质量较差。最近开发的眼科基础模型RETFound在视网膜图像识别任务中显示出更高的准确性。本研究利用社区筛查的真实世界图像，开发了一种用于多种眼病筛查的RETFound增强深度学习（DL）模型。我们的结果表明，与商业模型相比，我们的DL模型将灵敏度和特异性提高了15%以上。我们的模型还显示出比使用传统流程开发的AI模型更好的泛化能力。此外，决策曲线分析强调了在中国城乡地区使用我们的模型具有更高的净效益。这些发现表明，RETFound增强的DL模型在基于社区的筛查中可以实现更高的净效益，倡导在低收入和中等收入国家采用该模型以应对全球眼部健康挑战。
