Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases

WANG Yudian

doi:10.11959/j.issn.2096-3750.2025.00471

您当前的位置：

首页 >

文章列表页 >

Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases

Theory and Technology | 更新时间：2025-09-12

- Review of the application of deep learning in the auxiliary diagnosis of ophthalmic diseases
- Chinese Journal on Internet of Things Vol. 9, Issue 2, Pages: 172-189(2025)
- 作者机构：
  
  北京邮电大学国际学院，北京 100088
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2025.00471
  CLC： TP18
- Received：14 February 2025，
  
  Revised：2025-06-09，
  
  Published：10 June 2025
- 稿件说明：
移动端阅览
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

QUIGLEY H A . Number of people with glaucoma worldwide [J ] . British Journal of Ophthalmology , 1996 , 80 ( 5 ): 389 - 393 .

WU J D , FANG H H , LI F , et al . GAMMA challenge: glaucoma grAding from multi-modality imAges [J ] . Medical Image Analysis , 2023 , 90 : 102938 .

ALMAZROA A A , ALODHAYB S , OSMAN E , et al . Retinal fundus images for glaucoma analysis: the RIGA dataset [C ] // Proceedings of the Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications . SPIE , 2018 : 8 .

SIVASWAMY J , KRISHNADAS S R , DATT JOSHI G , et al . Drishti-GS: retinal image dataset for optic nerve head(ONH) segmentation [C ] // Proceedings of the 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) . Piscataway : IEEE Press , 2014 : 53 - 56 .

BAJWA M N , SINGH G A P , NEUMEIER W , et al . G1020 dataset [DB ] . 2023 .

TAMPA H , MEKONGO M , TIEDEU A . Deep learning-based algorithm for automated detection of glaucoma on eye fundus images [J ] . Multimedia Tools and Applications , 2024 : 1 - 18 .

SHARMA S K , MUDULI D , PRIYADARSHINI R , et al . An evolutionary supply chain management service model based on deep learning features for automated glaucoma detection using fundus images [J ] . Engineering Applications of Artificial Intelligence , 2024 , 128 : 107449 .

GOVINDAN M , DHAKSHNAMURTHY V K , SREERANGAN K , et al . A framework for early detection of glaucoma in retinal fundus images using deep learning [J ] . Engineering Proceedings , 2024 , 62 ( 1 ): 3 .

ALJOHANI A , ABURASAIN R Y . A hybrid framework for glaucoma detection through federated machine learning and deep learning models [J ] . BMC Medical Informatics and Decision Making , 2024 , 24 ( 1 ): 115 .

AL-BANDER B , AL-NUAIMY W , AL-TAEE M A , et al . Automated glaucoma diagnosis using deep learning approach [C ] // Proceedings of the 2017 14th International Multi-Conference on Systems , Signals & Devices (SSD) . Piscataway : IEEE Press , 2017 : 207 - 210 .

WANG R L , BRADLEY C , HERBERT P , et al . Deep learning-based identification of eyes at risk for glaucoma surgery [J ] . Scientific Reports , 2024 , 14 : 599 .

CHAI Y D , LIU H Y , XU J . Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models [J ] . Knowledge-Based Systems , 2018 , 161 : 147 - 156 .

CHIANG Y Y , CHEN C L , CHEN Y H . Deep learning evaluation of glaucoma detection using fundus photographs in highly myopic populations [J ] . Biomedicines , 2024 , 12 ( 7 ): 1394 .

MADADI Y , ABU-SERHAN H , YOUSEFI S . Domain adaptation-based deep learning model for forecasting and diagnosis of glaucoma disease [J ] . Biomedical Signal Processing and Control , 2024 , 92 : 106061 .

PANAHI A , ASKARI MOGHADAM R , TARVIRDIZADEH B , et al . Simplified U-Net as a deep learning intelligent medical assistive tool in glaucoma detection [J ] . Evolutionary Intelligence , 2024 , 17 ( 2 ): 1023 - 1034 .

AURANGZEB K , HAIDER S I , ALHUSSEIN M . Refined Anam-Net: lightweight deep learning model for improved segmentation performance of optic cup and disc for glaucoma diagnosis [J ] . Computers , Materials & Continua, 2024 , 80 ( 1 ): 1381 - 1405 .

KASHYAP R , NAIR R , GANGADHARAN S M P , et al . Glaucoma detection and classification using improved U-Net deep learning model [J ] . Healthcare , 2022 , 10 ( 12 ): 2497 .

SUDHAN M B , SINTHUJA M , PRAVINTH RAJA S , et al . Segmentation and classification of glaucoma using U-Net with deep learning model [J ] . Journal of Healthcare Engineering , 2022 , 2022 ( 1 ): 1601354 .

ABBAS Q . Glaucoma-deep: detection of glaucoma eye disease on retinal fundus images using deep learning [J ] . International Journal of Advanced Computer Science and Applications , 2017 , 8 ( 6 ): 79 - 85 .

ELAKKIYA B , SARANIYA O . A comparative analysis of pretrained and transfer-learning model for automatic diagnosis of glaucoma [C ] // Proceedings of the 2019 11th International Conference on Advanced Computing (ICoAC) . Piscataway : IEEE Press , 2019 : 167 - 172 .

WANG S , KIM B , KANG J , et al . Precision diagnosis of glaucoma with VLLM ensemble deep learning [J ] . Applied Sciences , 2024 , 14 ( 11 ): 4588 .

SHOUKAT A , AKBAR S , HASSAN S A , et al . Automatic diagnosis of glaucoma from retinal images using deep learning approach [J ] . Diagnostics , 2023 , 13 ( 10 ): 1738 .

HU W , WANG S Y . Predicting glaucoma progression requiring surgery using clinical free-text notes and transfer learning with transformers [J ] . Translational Vision Science & Technology , 2022 , 11 ( 3 ): 37 .

WONG W L , SU X Y , LI X , et al . Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis [J ] . The Lancet Global Health , 2014 , 2 ( 2 ): e106 - e116 .

YE H H , ZHANG Q , LIU X H , et al . Prevalence of age-related macular degeneration in an elderly urban Chinese population in China: the Jiangning Eye Study [J ] . Investigative Ophthalmology & Visual Science , 2014 , 55 ( 10 ): 6374 - 6380 .

许迅 , 刘堃 , 苏莉 . 《中国年龄相关性黄斑变性临床诊疗指南(2023年)》更新点 [J ] . 中华眼底病杂志 , 2023 ( 11 ): 879 - 882 .

XU X , LIU K , SU L . Update points in the Evidence-based guidelines for diagnosis and treatment of age-related macular degeneration in China(2023) [J ] . Chinese Journal of Ocular Fundus Diseases , 2023 ( 11 ): 879 - 882 .

FANG H H , LI F , FU H Z , et al . ADAM challenge: detecting age-related macular degeneration from fundus images [J ] . IEEE Transactions on Medical Imaging , 2022 , 41 ( 10 ): 2828 - 2847 .

National Eye Institute of the United State . NEI age-related eye disease study (AREDS) [DB ] . 2023 .

PACHADE S , PORWAL P , THULKAR D , et al . Retinal fundus multi-disease image dataset (RFMiD) [DB ] . IEEE Dataport , 2020 . doi: 10.21227/s3g7-st65 http://dx.doi.org/10.21227/s3g7-st65 .

GHOLAMI P , ROY P , LAKSHMINARAYANAN V . Age-related macular degeneration retinal OCT images [DB ] . ( 2018 )

SENDECKI A , LEDWOŃ D , TUSZY A , et al . Fundus image deep learning study to explore the association of retinal morphology with age-related macular degeneration polygenic risk score [J ] . Biomedicines , 2024 , 12 ( 9 ): 2092 .

SENDECKI A , LEDWOŃ D , NYCZ J , et al . A deep learning approach to explore the association of age-related macular degeneration polygenic risk score with retinal optical coherence tomography: a preliminary study [J ] . Acta Ophthalmologica , 2024 , 102 ( 7 ): e1029 - e1039 .

LEE C S , BAUGHMAN D M , LEE A Y . Deep learning is effective for classifying normal versus age-related macular degeneration OCT images [J ] . Ophthalmology Retina , 2017 , 1 ( 4 ): 322 - 327 .

SAHA S , NASSISI M , WANG M , et al . Automated detection and classification of early AMD biomarkers using deep learning [J ] . Scientific Reports , 2019 , 9 : 10990 .

MATHIEU A , AJANA S , KOROBELNIK J F , et al . DeepAlienorNet: a deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration [J ] . Acta Ophthalmologica , 2024 , 102 ( 5 ): e823 - e830 .

PENG Y F , DHARSSI S , CHEN Q Y , et al . DeepSeeNet: a deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs [J ] . Ophthalmology , 2019 , 126 ( 4 ): 565 - 575 .

ALI M A , HOSSAIN M S , HOSSAIN M K , et al . AMDNet23: Hybrid CNN-LSTM deep learning approach with enhanced preprocessing for age-related macular degeneration (AMD) detection [J ] . Intelligent Systems with Applications , 2024 , 21 : 200334 .

JUNG J , HAN J , HAN J M , et al . Prediction of neovascular age-related macular degeneration recurrence using optical coherence tomography images with a deep neural network [J ] . Scientific Reports , 2024 , 14 : 5854 .

KIM N , LEE M , CHUNG H , et al . Prediction of post-treatment visual acuity in age-related macular degeneration patients with an interpretable machine learning method [J ] . Translational Vision Science & Technology , 2024 , 13 ( 9 ): 3 .

CHEN R Y , ZHANG W Y , SONG F , et al . Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening [J ] . NPJ Digital Medicine , 2024 , 7 : 34 .

SAßMANNSHAUSEN M , BEHNING C , WEINZ J , et al . Characteristics and spatial distribution of structural features in age-related macular degeneration: a MACUSTAR study report [J ] . Ophthalmology Retina , 2023 , 7 ( 5 ): 420 - 430 .

GAO Q T , AMASON J , RATHINAVELU J , et al . Deep learning for longitudinal OCT retinal layer thickness and AMD progression prediction [J ] . Investigative Ophthalmology & Visual Science , 2023 , 64 ( 8 ).

RICARDI F , OAKLEY J , RUSSAKOFF D , et al . Validation of a deep learning model for automatic detection and quantification of five OCT critical retinal features associated with neovascular age-related macular degeneration [J ] . British Journal of Ophthalmology , 2024 , 108 ( 10 ): 1436 - 1442 .

THOMAS A , HARIKRISHNAN P M , KRISHNA A K , et al . A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images [J ] . Biomedical Signal Processing and Control , 2021 , 67 : 102538 .

刘雪立 . 全球糖尿病视网膜病变患病率和2045年负担预测: 美国眼科学会报告要点摘编 [J ] . 眼科新进展 , 2024 , 44 ( 1 ): 80 - 81 .

LIU X L . Global prevalence of diabetic retinopathy and its burden forecast in 2045: excerpts from the report of American Ophthalmology Association [J ] . Recent Advances in Ophthalmology , 2024 , 44 ( 1 ): 80 - 81 .

LI T , GAO Y Q , WANG K , et al . Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening [J ] . Information Sciences , 2019 , 501 : 511 - 522 .

DECENCIÈRE E , ZHANG X W , CAZUGUEL G , et al . Feedback on a publicly distributed image database: the messidor database [J ] . Image Analysis & Stereology , 2014 , 33 ( 3 ): 231 .

PORWAL P , PACHADE S , KAMBLE R , et al . Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research [J ] . Data , 2018 , 3 ( 3 ): 25 .

Kaggle diabetic retinopathy detection training dataset (DRD) [DB ] . 2019 .

JEBA D D , PRIESTLY S B . Diabetic retinopathy-fundus image dataset (AGAR300) [DB ] . 2020 . DOI: 10.21227/fsnq-tn19 http://dx.doi.org/10.21227/fsnq-tn19 .

SIVAPRIYA G , MANJULA DEVI R , KEERTHIKA P , et al . Automated diagnostic classification of diabetic retinopathy with microvascular structure of fundus images using deep learning method [J ] . Biomedical Signal Processing and Control , 2024 , 88 : 105616 .

DAI L , SHENG B , CHEN T L , et al . A deep learning system for predicting time to progression of diabetic retinopathy [J ] . Nature Medicine , 2024 , 30 ( 2 ): 584 - 594 .

GUEFRACHI S , ECHTIOUI A , HAMAM H . Automated diabetic retinopathy screening using deep learning [J ] . Multimedia Tools and Applications , 2024 , 83 ( 24 ): 65249 - 65266 .

SOOMRO T A , GAO J B , KHAN M A U , et al . Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy [C ] // Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) . Piscataway : IEEE Press , 2016 : 1 - 8 .

JENA P K , KHUNTIA B , PALAI C , et al . A novel approach for diabetic retinopathy screening using asymmetric deep learning features [J ] . Big Data and Cognitive Computing , 2023 , 7 ( 1 ): 25 .

PRABHAKAR T , MADHUSUDHANA RAO T V , MARAM B , et al . Exponential gannet firefly optimization algorithm enabled deep learning for diabetic retinopathy detection [J ] . Biomedical Signal Processing and Control , 2024 , 87 : 105376 .

SARANYA P , UMAMAHESWARI K M . Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model [J ] . Multimedia Tools and Applications , 2024 , 83 ( 17 ): 52253 - 52273 .

QUMMAR S , KHAN F G , SHAH S , et al . A deep learning ensemble approach for diabetic retinopathy detection [J ] . IEEE Access , 2019 , 7 : 150530 - 150539 .

LALITHADEVI B , KRISHNAVENI S . Diabetic retinopathy detection and severity classification using optimized deep learning with explainable AI technique [J ] . Multimedia Tools and Applications , 2024 , 83 ( 42 ): 89949 - 90013 .

MANSOUR R F . Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy [J ] . Biomedical Engineering Letters , 2018 , 8 ( 1 ): 41 - 57 .

WANG M F , GONG Q Z , WAN Q , et al . A fast interpretable adaptive meta-learning enhanced deep learning framework for diagnosis of diabetic retinopathy [J ] . Expert Systems with Applications , 2024 , 244 : 123074 .

SINGH S P , GUPTA P , DUNG R . Diabetic retinopathy detection by fundus images using fine tuned deep learning model [J ] . Multimedia Tools and Applications , 2024 , 83 ( 39 ): 86657 - 86679 .

任志远，基于深度学习的糖尿病视网膜病变分类方法研究 [D ] . 齐鲁工业大学， 2024 .

REN Z Y . Research on classification methods of diabetic retinopathy based on deep learning [D ] . Qilu University of Technology , 2024 .

LIM W X , CHEN Z Y . Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G [J ] . Medical & Biological Engineering & Computing , 2024 , 62 ( 8 ): 2571 - 2583 .

ASBELL P , DUALAN I , MINDEL J , et al . Age-related cataract [J ] . The Lancet , 2005 , 365 ( 9459 ): 599 - 609 .

GUPTA V B , RAJAGOPALA M , RAVISHANKAR B . Etiopathogenesis of cataract: an appraisal [J ] . Indian Journal of Ophthalmology , 2014 , 62 ( 2 ): 103 - 110 .

颜钰玲 , 薛春燕 . 人工智能在白内障诊断的应用进展 [J ] . 眼科学报 , 2024 , 39 ( 3 ): 160 - 168 .

YAN Y L , XUE C Y . Advances in artificial intelligence for cataract diagnosis [J ] . Eye Science , 2024 , 39 ( 3 ): 160 - 168 .

AL HAJJ H , LAMARD M , CONZE P H , et al . CATARACTS: Challenge on automatic tool annotation for cataRACT surgery [J ] . Medical Image Analysis , 2019 , 52 : 24 - 41 .

ZHAI Y X , ZHANG G H , ZHENG L S , et al . Cataract surgery dataset for eye positioning and alignment [DB ] . IEEE Dataport , 2021 . doi: 10.21227/1v4j-9f35 http://dx.doi.org/10.21227/1v4j-9f35 .

SCHWARTZ K . Ocular disease intelligent recognition [DB ] . 2023 .

KUMARI P , SAXENA P . Cataract detection and visualization based on multi-scale deep features by RINet tuned with cyclic learning rate hyperparameter [J ] . Biomedical Signal Processing and Control , 2024 , 87 : 105452 .

ZIA A , MAHUM R , AHMAD N , et al . Eye diseases detection using deep learning with BAM attention module [J ] . Multimedia Tools and Applications , 2024 , 83 ( 20 ): 59061 - 59084 .

DONG Y Y , ZHANG Q Y , QIAO Z Q , et al . Classification of cataract fundus image based on deep learning [C ] // Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST) . Piscataway : IEEE Press , 2017 : 1 - 5 .

FAHMI F , ESTIKO H . Cataract detection using deep learning in teleophthalmology systems [C ] // Proceedings of the 2024 14th International Conference on Electrical Engineering (ICEENG) . Piscataway : IEEE Press , 2024 : 260 - 264 .

JIANG J W , ZHANG Y , XIE H , et al . A deep learning based fine-grained classification algorithm for grading of visual impairment in cataract patients [J ] . Optoelectronics Letters , 2024 , 20 ( 1 ): 48 - 57 .

RAN J , NIU K , HE Z Q , et al . Cataract detection and grading based on combination of deep convolutional neural network and random forests [C ] // Proceedings of the 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) . Piscataway : IEEE Press , 2018 : 155 - 159 .

YANG M M , YANG J J , ZHANG Q Y , et al . Classification of retinal image for automatic cataract detection [C ] // Proceedings of the 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013) . Piscataway : IEEE Press , 2013 : 674 - 679 .

ZHANG H Y , NIU K , XIONG Y M , et al . Automatic cataract grading methods based on deep learning [J ] . Computer Methods and Programs in Biomedicine , 2019 , 182 : 104978 .

GU Y Y , FANG L X , MOU L , et al . A ranking-based multi-scale feature calibration network for nuclear cataract grading in AS-OCT images [J ] . Biomedical Signal Processing and Control , 2024 , 90 : 105836 .

YADAV S , YADAV J K P S . Automatic cataract severity detection and grading using deep learning [J ] . Journal of Sensors , 2023 ( 1 ): 2973836 .

Views

126

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

AI-assisted ransomware：operating principles and defense methods

Feature fusion method for UWB angle of arrival estimation under non-line-of-sight conditions

Lightweight parallel denoising network-based OTFS channel estimation algorithm

A 3D printing defect detection algorithm incorporating attention mechanism

Related Author

Li Yeshen

Dong Peng

Zhu He

Guo Xiaotian

Yin Chenxu

Xiong Ke

ZENG Wenmin

ZHENG Bofeng

Related Institution

Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education, Beijing Jiaotong University

School of Computer Science and Technology, Beijing Jiaotong University

Network Security Research Office, China Railway Information (Beijing) Network Technology Research Institute Co., Ltd.

Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Harbin Institute of Technology

IOT Thrust, The Hong Kong University of Science and Technology

⁰