深度学习助力眼科疾病诊断，提升准确率与治疗效率
Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends
- 融合CNN、注意力机制与Transformer的先进模型用于眼病分析
- 显著提升糖尿病视网膜病变等疾病的诊断准确率
- 适合眼科医生与AI医疗研究者参考，关注临床落地挑战
人工智能，特别是深度学习（DL），正推动眼科学进入新纪元，为后段眼病的诊断与治疗带来变革性潜力。本文综述了深度学习在糖尿病视网膜病变、青光眼、老年黄斑变性及视网膜血管分割等疾病中的前沿应用。系统介绍了基础机器学习方法与先进深度学习架构，如卷积神经网络（CNN）、注意力机制和基于Transformer的模型，强调其在提升诊断精度、优化治疗策略及改善患者照护方面的关键作用。同时，探讨了将AI融入临床实践的关键挑战，包括数据多样性保障、算法透明度提升及多模态数据的有效利用。文章指出，尽管AI在疾病诊断与患者护理中展现出巨大潜力，仍需多方协作克服障碍，以充分释放其在眼科领域的价值。
原文摘要 · Abstract (English)
The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular conditions, including diabetic retinopathy, glaucoma, age-related macular degeneration, and retinal vessel segmentation. We provide a comprehensive overview of foundational ML techniques and advanced DL architectures, such as CNNs, attention mechanisms, and transformer-based models, highlighting the evolving role of AI in enhancing diagnostic accuracy, optimizing treatment strategies, and improving overall patient care. Additionally, we present key challenges in integrating AI solutions into clinical practice, including ensuring data diversity, improving algorithm transparency, and effectively leveraging multimodal data. This review emphasizes AI's potential to improve disease diagnosis and enhance patient care while stressing the importance of collaborative efforts to overcome these barriers and fully harness AI's impact in advancing eye care.
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