人工智能在糖尿病视网膜病变应用的研究进展
Research Progress of Artificial Intelligence Application in Diabetic Retinopathy
DOI: 10.12677/acm.2024.143866, PDF,    科研立项经费支持
作者: 陈艺璇：甘肃中医药大学第一临床医学院，甘肃 兰州
关键词: 人工智能；糖尿病视网膜病变；Artificial Intelligence； Diabetic Retinopathy
摘要: 糖尿病视网膜病变(DR)是常见的糖尿病严重的并发症之一，是导致全球中老年人失明的几大重要原因之一，然而，通过早期发现和积极治疗，可以有效地降低致盲的风险。近些年来，人工智能(AI)的快速发展为DR的筛查带来新的可能性。AI能够节约时间，提升诊疗的效率，减轻DR带来的损害。本文综述了AI在DR领域的应用现状、揭示了相关问题，并展望了未来的发展方向。
Abstract: Diabetic retinopathy (DR) is a common and severe complication of diabetes, ranking among the leading causes of blindness in the elderly worldwide. However, through early detection and proactive treatment, the risk of blindness can be effectively reduced. In recent years, the rapid development of artificial intelligence (AI) has brought new possibilities to the screening of DR. AI has the potential to save time, enhance the efficiency of diagnosis and treatment, and alleviate the damage caused by DR. This article reviews the current state of AI applications in the field of DR, highlights associated challenges, and outlines future development directions.
文章引用：陈艺璇. 人工智能在糖尿病视网膜病变应用的研究进展[J]. 临床医学进展, 2024, 14(3): 1462-1467. https://doi.org/10.12677/acm.2024.143866

参考文献

[1] Bourne, R.R.A., Stevens, G.A., White, R.A., et al. (2013) Causes of Vision Loss Worldwide, 1990-2010: A Systematic Analysis. The Lancet Global Health, 1, e339-e349. [Google Scholar] [CrossRef] 
[2] Song, P., Yu, J., Chan, K.Y., et al. (2018) Prevalence, Risk Factors and Burden of Diabetic Retinopathy in China: A Systematic Review and Meta-Analysis. Journal of Global Health, 8, Article 010803. [Google Scholar] [CrossRef] [PubMed]
[3] Wong, T.Y., Sun, J., Kawasaki, R., et al. (2018) Guidelines on Diabetic Eye Care: The International Council of Ophthalmology Recommendations for Screening, Follow-Up, Referral, and Treatment Based on Resource Settings. Ophthalmology, 125, 1608-1622. [Google Scholar] [CrossRef] [PubMed]
[4] 王弈, 李传富. 人工智能方法在医学图像处理中的研究新进展[J]. 中国医学物理学杂志, 2013, 30(3): 4138-4143.
[5] Gibson, D.M. (2012) Diabetic Retinopathy and Age-Related Macular Degeneration in the U.S. American Journal of Preventive Medicine, 43, 48-54. [Google Scholar] [CrossRef] [PubMed]
[6] Eppley, S.E., Mansberger, S.L., Ramanathan, S., et al. (2019) Characteristics Associated with Adherence to Annual Dilated Eye Examinations among US Patients with Diagnosed Diabetes. Ophthalmology, 126, 1492-1499. [Google Scholar] [CrossRef] [PubMed]
[7] 彭金娟, 邹海东, 王伟伟, 等. 上海市北新泾社区糖尿病视网膜病变远程筛查系统的应用研究[J]. 中华眼科杂志, 2010, 46(3): 258-262.
[8] Johansen, M.A., Fossen, K., Norum, J., et al. (2008) The Potential of Digital Monochrome Images versus Colour Slides in Telescreening for Diabetic Retinopathy. Journal of Telemedicine and Telecare, 14, 27-31. [Google Scholar] [CrossRef] [PubMed]
[9] Le, C.Y., Bengio, Y., Hinton, G., et al. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[10] U.S. Food and Drug Administration (2018) FDA Permits Marketing of Artificial Intelligence-Based Device to Detect Certain Diabetes-Related Eye Problems.
https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye
[11] Abramoff, M.D., Lavin, P.T., Birch, M., et al. (2018) Pivotal Trial of an Autonomous AI-Based Diagnostic System for Detection of Diabetic Retinopathy in Primary Care Offices. npj Digital Medicine, 1, Article No. 39. [Google Scholar] [CrossRef] [PubMed]
[12] Ting, D.S.W., Cheung, C.Y., Lim, G., et al. (2017) Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes. JAMA, 318, 2211-2223. [Google Scholar] [CrossRef] [PubMed]
[13] Ting, D.S.W., Cheung, C.Y., Nguyen, Q., et al. (2019) Deep Learning in Estimating Prevalence and Systemic Risk Factors for Diabetic Retinopathy: A Multi-Ethnic Study. npj Digital Medicine, 2, Article No. 24. [Google Scholar] [CrossRef] [PubMed]
[14] Gulshan, V., Peng, L., Coram, M., et al. (2016) Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316, 2402-2410. [Google Scholar] [CrossRef] [PubMed]
[15] Gargeya, R. and Leng, T. (2017) Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology, 124, 962-969. [Google Scholar] [CrossRef] [PubMed]
[16] Hsieh, Y.T., Chuang, L.M., Jiang, Y.D., et al. (2021) Application of Deep Learning Image Assessment Software Veriseetm for Diabetic Retinopathy Screening. Journal of the Formosan Medical Association, 120, 165-171. [Google Scholar] [CrossRef] [PubMed]
[17] Kanagasingam, Y., Xiao, D., Vignarajan, J., et al. (2018) Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care. JAMA Network, 1, e182665. [Google Scholar] [CrossRef] [PubMed]
[18] Karakaya, M. and Hacisoftaoglu, R.E. (2020) Comparison of Smartphone-Basedretinal Imaging Systems for Diabetic Retinopathy Detection Using Deep Learning. BMC Bioinformatics, 21, Article No. 259. [Google Scholar] [CrossRef] [PubMed]
[19] Tan, G.S., Cheung, N., Simó, R., et al. (2016) Diabetic Macular Oedema. The Lancet Diabetes & Endocrinology, 5, 143-155. [Google Scholar] [CrossRef] 
[20] Adhi, M. and Duker, J.S. (2013) Optical Coherence Tomography—Current and Future Applications. Current Opinion in Ophthalmology, 24, 213-221. [Google Scholar] [CrossRef] 
[21] Rajalakshmi, R., Subashini, R., Anjana, R.M., et al. (2018) Automated Diabetic Retinopathy Detection in Smartphone-Based Fundus Photography Using Artificial Intelligence. Eye, 32, 1138-1144. [Google Scholar] [CrossRef] [PubMed]
[22] He, J., Baxter, S.L., Xu, J., et al. (2019) The Practical Implementation of Artificial Intelligence Technologies in Medicine. Nature Medicine, 25, 30-36. [Google Scholar] [CrossRef] [PubMed]
[23] Xu, J., Xue, K.M., Zhang, K., et al. (2019) Current Status and Future Trends of Clinical Diagnoses via Image-Based Deep Learning. Theranostics, 9, 7556-7565. [Google Scholar] [CrossRef] [PubMed]
[24] 陶梦璋, 王雨生. 人工智能医学影像分析在眼科学领域应用的现状和展望[J]. 国际眼科纵览, 2018, 42(1): 1-5.

为你推荐