深度学习在眼科疾病辅助诊断中的应用综述

王与点

doi:10.11959/j.issn.2096-3750.2025.00471

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深度学习在眼科疾病辅助诊断中的应用综述

理论与技术 | 更新时间：2025-09-12

- 深度学习在眼科疾病辅助诊断中的应用综述
- Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases
- 物联网学报 2025年9卷第2期页码：172-189
- 作者机构：
  
  北京邮电大学国际学院，北京 100088
- 作者简介：
  
  [ "王与点（2003‒ ），男，北京邮电大学国际学院在读，主要研究方向为物联网工程、人工智能、深度学习。" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2025.00471
  中图分类号： TP18
- 收稿：2025-02-14，
  
  修回：2025-06-09，
  
  纸质出版：2025-06-10
- 稿件说明：
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王与点.深度学习在眼科疾病辅助诊断中的应用综述[J].物联网学报,2025,09(02):172-189.

WANG Yudian.Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases[J].Chinese Journal on Internet of Things,2025,09(02):172-189.
王与点.深度学习在眼科疾病辅助诊断中的应用综述[J].物联网学报,2025,09(02):172-189. DOI： 10.11959/j.issn.2096-3750.2025.00471.

WANG Yudian.Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases[J].Chinese Journal on Internet of Things,2025,09(02):172-189. DOI： 10.11959/j.issn.2096-3750.2025.00471.

摘要

随着人工智能等技术的高速进步，深度学习等方法在医学领域的研究应用范围和影响力日益扩大，新型技术与临床实践的结合也成为近年来的研究热点，在眼科疾病的辅助诊断中展现出了巨大的潜力。基于卷积神经网络的深度学习算法在疾病筛查、病灶检测、组织分割等任务中表现出了巨大的潜力，并逐渐用于青光眼、老年黄斑变性、糖尿病性视网膜疾病、白内障等多种眼科疾病的诊断和筛查。首先，就深度学习在眼科疾病辅助诊断中的相关工作和应用做了综述，重点介绍了各类眼科疾病的数据集、评价指标和当前的研究进展。然后，得出结论：深度学习在眼科疾病诊断中取得了显著成果，但仍面临数据集规模小、类别不平衡、模型可解释性不足等挑战。最后，对未来前景和发展做了展望。

Abstract

With the rapid advancement of technologies

such as artificial intelligence

the research and application scope of methods such as deep learning in the medical field are expanding increasingly

and the combination of new technologies and clinical practice has become a research hotspot in recent years

demonstrating great potential in the auxiliary diagnosis of ophthalmic diseases. Deep learning algorithms based on convolutional neural networks have shown great potential in tasks

such as disease screening

lesion detection

and tissue segmentation

and have been gradually applied to the diagnosis and screening of various ophthalmic diseases

including glaucoma

age-related macular degeneration

diabetic retinopathy

and cataracts. Firstly

the related works and applications of deep learning in the auxiliary diagnosis of ophthalmic diseases were reviewed

focusing on the datasets

evaluation metrics

and current research progress of various ophthalmic diseases. Secondly

it was found that deep learning achieved remarkable results in the diagnosis of ophthalmic diseases

but it still faced challenges such as small dataset size

class imbalance

and insufficient model interpretability. Finally

the future prospects and development were also discussed.

关键词

Keywords

references

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