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基于人工智能和眼底图像诊断视网膜血管疾病的研究进展

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.

作者信息

Ji Yuke, Ji Yun, Liu Yunfang, Zhao Ying, Zhang Liya

机构信息

The Laboratory of Artificial Intelligence and Bigdata in Ophthalmology, Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.

Affiliated Hospital of Shandong University of traditional Chinese Medicine, Jinan, Shandong, China.

出版信息

Front Cell Dev Biol. 2023 Mar 28;11:1168327. doi: 10.3389/fcell.2023.1168327. eCollection 2023.


DOI:10.3389/fcell.2023.1168327
PMID:37056999
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10086262/
Abstract

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

摘要

作为全身唯一能够直接观察到的血管,视网膜血管的病变与全身及多个系统的代谢状态相关,严重影响患者的视力和生活质量。及时诊断和治疗是改善视力预后的关键。近年来,随着人工智能的快速发展,人工智能在眼科领域的应用日益广泛和深入,尤其是在视网膜血管疾病领域。基于人工智能和眼底图像的研究成果显著,为早期诊断和治疗提供了极大的可能性。本文综述了人工智能在视网膜血管疾病(包括糖尿病性视网膜病变、高血压性视网膜病变、视网膜静脉阻塞、早产儿视网膜病变和年龄相关性黄斑变性)方面的最新研究进展。同时也讨论了研究过程中的局限性和挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0133/10086262/5efaafae15e5/fcell-11-1168327-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0133/10086262/063570a9a8a3/fcell-11-1168327-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0133/10086262/5efaafae15e5/fcell-11-1168327-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0133/10086262/063570a9a8a3/fcell-11-1168327-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0133/10086262/5efaafae15e5/fcell-11-1168327-g002.jpg

相似文献

[1]
Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.

Front Cell Dev Biol. 2023-3-28

[2]
Artificial intelligence in ophthalmology.

Rom J Ophthalmol. 2023

[3]
Application of artificial intelligence in ophthalmology.

Int J Ophthalmol. 2018-9-18

[4]
Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Medicina (Kaunas). 2024-3-23

[5]
Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

Transl Vis Sci Technol. 2024-4-2

[6]
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Curr Drug Targets. 2020

[7]
Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study.

Lancet Digit Health. 2019-5

[8]
Multimodal imaging interpreted by graders to detect re-activation of diabetic eye disease in previously treated patients: the EMERALD diagnostic accuracy study.

Health Technol Assess. 2021-5

[9]
Advances in artificial intelligence applications for ocular surface diseases diagnosis.

Front Cell Dev Biol. 2022-12-20

[10]
Survey on recent developments in automatic detection of diabetic retinopathy.

J Fr Ophtalmol. 2021-3

引用本文的文献

[1]
Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics.

Adv Ophthalmol Pract Res. 2025-7-14

[2]
Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.

Med Hypothesis Discov Innov Ophthalmol. 2025-5-10

[3]
Molecular and Cellular Mechanisms Involved in the Pathophysiology of Retinal Vascular Disease-Interplay Between Inflammation and Oxidative Stress.

Int J Mol Sci. 2024-11-4

[4]
Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis.

Medicine (Baltimore). 2024-7-19

[5]
Retinal Vein Occlusion-Background Knowledge and Foreground Knowledge Prospects-A Review.

J Clin Med. 2024-7-5

[6]
Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.

Int J Ophthalmol. 2024-6-18

[7]
Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.

Int J Ophthalmol. 2024-1-18

[8]
Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation.

Diagnostics (Basel). 2023-11-1

[9]
Attention-guided cascaded network with pixel-importance-balance loss for retinal vessel segmentation.

Front Cell Dev Biol. 2023-5-9

本文引用的文献

[1]
Analysis of Systemic and Serum Risk Factors in Patients with Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy.

Ophthalmol Ther. 2023-4

[2]
Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.

Diagnostics (Basel). 2023-1-16

[3]
Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score.

JAMA Netw Open. 2023-1-3

[4]
Incidence and multimodal imaging characteristics of macular neovascularisation subtypes in Chinese neovascular age-related macular degeneration patients.

Br J Ophthalmol. 2024-2-21

[5]
The Impact of Residual Retinal Fluid Following Intravitreal Anti-Vascular Endothelial Growth Factor Therapy for Diabetic Macular Edema and Macular Edema Secondary to Retinal Vein Occlusion: A Systematic Review.

Ophthalmic Surg Lasers Imaging Retina. 2023-1

[6]
Advances in artificial intelligence applications for ocular surface diseases diagnosis.

Front Cell Dev Biol. 2022-12-20

[7]
Classification of diabetic retinopathy: Past, present and future.

Front Endocrinol (Lausanne). 2022

[8]
Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.

Eye (Lond). 2023-8

[9]
Category weighted network and relation weighted label for diabetic retinopathy screening.

Comput Biol Med. 2023-1

[10]
Applications of artificial intelligence in forensic sciences: Current potential benefits, limitations and perspectives.

Int J Legal Med. 2023-3

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