人工智能在青光眼中的应用现状及展望
Current Status and Prospects of Artificial Intelligence in Glaucoma Applications
DOI: 10.12677/acm.2025.1592655, PDF,   
作者: 白 天, 余欣含：延安大学医学院，陕西 延安；张二飞*：延安大学附属医院麻醉与围术期医学科，陕西 延安
关键词: 机器学习；深度学习；青光眼；筛查；诊断；Machine Learning； Deep Learning； Glaucoma； Screening； Diagnosis
摘要: 青光眼是造成全球范围内不可逆性失明的主要原因，是一组以视神经损伤和视野缺损为主要特征的疾病，病理性眼压升高是其主要危险因素。如何在普通人群中筛查出可疑青光眼人群、明确诊断出青光眼早期患者、密切监测青光眼病情进展与临床变化等方面是当下人们在青光眼临床研究中的攻克方向。人工智能(Artificial Intelligence, AI)是引导第四次工业革命的中坚力量，在过去几十年的发展中AI和医学走上了相辅相成的道路：AI技术的进步不断更新健康的概念，对健康的追求又影响着技术的发展。机器学习(Machine Learning, ML)、深度学习(Deep Learning, DL)，人工神经网络(Artificial Neural Network, ANN)等技术的日臻成熟，推进了青光眼诊疗方面的进步。本综述概述了AI技术的发展，总结了其在青光眼临床应用的最新进展，讨论了当下存在的挑战及对未来的展望。
Abstract: Glaucoma is a major cause of irreversible blindness worldwide, a group of diseases characterized by optic nerve damage and visual field defects, and pathologically elevated intraocular pressure is its main risk factor. It is the current direction of clinical research in glaucoma to screen for suspected glaucoma in the general population, diagnose early glaucoma patients, and closely monitor the progression and clinical changes of glaucoma. Artificial Intelligence (AI) is the backbone of the fourth industrial revolution, and in the past few decades AI and medicine have embarked on a complementary path: advances in AI technology are constantly updating the concept of health, and the pursuit of health is influencing the development of technology. Machine Learning (ML), Deep Learning (DL), and Artificial Neural Network (ANN) technologies have become more and more matured and advanced the treatment of glaucoma. This review provides an overview of the development of AI technologies, summarizes the latest advances in their clinical applications in glaucoma, and discusses the current challenges and future prospects.
文章引用：白天, 余欣含, 张二飞. 人工智能在青光眼中的应用现状及展望[J]. 临床医学进展, 2025, 15(9): 1556-1562. https://doi.org/10.12677/acm.2025.1592655

参考文献

[1] Song, P., Wang, J., Bucan, K., Theodoratou, E., Rudan, I. and Chan, K.Y. (2017) National and Subnational Prevalence and Burden of Glaucoma in China: A Systematic Analysis. Journal of Global Health, 7, Article 020705. [Google Scholar] [CrossRef] [PubMed]
[2] Hamet, P. and Tremblay, J. (2017) Artificial Intelligence in Medicine. Metabolism, 69, S36-S40. [Google Scholar] [CrossRef] [PubMed]
[3] Kolluri, S., Lin, J., Liu, R., Zhang, Y. and Zhang, W. (2022) Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: A Review. The AAPS Journal, 24, Article No. 19. [Google Scholar] [CrossRef] [PubMed]
[4] Shafique, M., Theocharides, T., Bouganis, C., Hanif, M.A., Khalid, F., Hafiz, R., et al. (2018) An Overview of Next-Generation Architectures for Machine Learning: Roadmap, Opportunities and Challenges in the IOT Era. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, 19-23 March 2018, 827-832. [Google Scholar] [CrossRef] 
[5] Goldstein, B.A., Navar, A.M. and Carter, R.E. (2017) Moving Beyond Regression Techniques in Cardiovascular Risk Prediction: Applying Machine Learning to Address Analytic Challenges. European Heart Journal, 38, ehw302. [Google Scholar] [CrossRef] [PubMed]
[6] Vranas, K.C., Jopling, J.K., Sweeney, T.E., Ramsey, M.C., Milstein, A.S., Slatore, C.G., et al. (2017) Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach. Critical Care Medicine, 45, 1607-1615. [Google Scholar] [CrossRef] [PubMed]
[7] Kuwahara, T., Hara, K., Mizuno, N., Haba, S., Okuno, N., Koda, H., et al. (2021) Current Status of Artificial Intelligence Analysis for Endoscopic Ultrasonography. Digestive Endoscopy, 33, 298-305. [Google Scholar] [CrossRef] [PubMed]
[8] Thompson, A.C., Jammal, A.A. and Medeiros, F.A. (2020) A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression. Translational Vision Science & Technology, 9, Article 42. [Google Scholar] [CrossRef] [PubMed]
[9] Rajalakshmi, R., Dutt, S., Sivaraman, A. and Savoy, F. (2020) Insights into the Growing Popularity of Artificial Intelligence in Ophthalmology. Indian Journal of Ophthalmology, 68, 1339-1346. [Google Scholar] [CrossRef] [PubMed]
[10] LeCun, Y., Bengio, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[11] Zhao, Q. and Hastie, T. (2019) Causal Interpretations of Black-Box Models. Journal of Business & Economic Statistics, 39, 272-281. [Google Scholar] [CrossRef] [PubMed]
[12] Ting, D.S.W., Pasquale, L.R., Peng, L., Campbell, J.P., Lee, A.Y., Raman, R., et al. (2019) Artificial Intelligence and Deep Learning in Ophthalmology. British Journal of Ophthalmology, 103, 167-175. [Google Scholar] [CrossRef] [PubMed]
[13] Avila-Tomás, J.F., Mayer-Pujadas, M.A. and Quesada-Varela, V.J. (2020) La inteligencia artificial y sus aplicaciones en medicina I: introducción antecedentes a la IA y robótica. Atención Primaria, 52, 778-784. [Google Scholar] [CrossRef] [PubMed]
[14] Fu, J., Zhong, X., Li, N., Van Dams, R., Lewis, J., Sung, K., et al. (2020) Deep Learning-Based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer. Physics in Medicine & Biology, 65, Article 075001. [Google Scholar] [CrossRef] [PubMed]
[15] Zhang, B., Liang, X.L., Gao, H.Y., Ye, L.S. and Wang, Y.G. (2016) Models of Logistic Regression Analysis, Support Vector Machine, and Back-Propagation Neural Network Based on Serum Tumor Markers in Colorectal Cancer Diagnosis. Genetics and Molecular Research, 15, gmr.15028643. [Google Scholar] [CrossRef] [PubMed]
[16] John, D. and Parikh, R. (2017) Cost-Effectiveness and Cost Utility of Community Screening for Glaucoma in Urban India. Public Health, 148, 37-48. [Google Scholar] [CrossRef] [PubMed]
[17] Bhartiya, S. (2022) Glaucoma Screening: Is AI the Answer? Journal of Current Glaucoma Practice, 16, 71-73. [Google Scholar] [CrossRef] [PubMed]
[18] Xiao, X., Xue, L., Ye, L., Li, H. and He, Y. (2021) Health Care Cost and Benefits of Artificial Intelligence-Assisted Population-Based Glaucoma Screening for the Elderly in Remote Areas of China: A Cost-Offset Analysis. BMC Public Health, 21, Article No. 1065. [Google Scholar] [CrossRef] [PubMed]
[19] Olawoye, O., Azuara-Blanco, A., Chan, V.F., Piyasena, P., Crealey, G.E., O’Neill, C., et al. (2021) A Review to Populate a Proposed Cost-Effectiveness Analysis of Glaucoma Screening in Sub-Saharan Africa. Ophthalmic Epidemiology, 29, 328-338. [Google Scholar] [CrossRef] [PubMed]
[20] Ting, D.S.W., Cheung, C.Y., Lim, G., Tan, G.S.W., Quang, N.D., Gan, A., 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]
[21] Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., et al. (2019) Deep Learning versus Human Graders for Classifying Diabetic Retinopathy Severity in a Nationwide Screening Program. npj Digital Medicine, 2, Article No. 25. [Google Scholar] [CrossRef] [PubMed]
[22] Tan, N.Y.Q., Friedman, D.S., Stalmans, I., Ahmed, I.I.K. and Sng, C.C.A. (2020) Glaucoma Screening: Where Are We and Where Do We Need to Go? Current Opinion in Ophthalmology, 31, 91-100. [Google Scholar] [CrossRef] [PubMed]
[23] Tang, J., Liang, Y., O'Neill, C., Kee, F., Jiang, J. and Congdon, N. (2019) Cost-effectiveness and Cost-Utility of Population-Based Glaucoma Screening in China: A Decision-Analytic Markov Model. The Lancet Global Health, 7, e968-e978. [Google Scholar] [CrossRef] [PubMed]
[24] John, D. and Parikh, R. (2018) Cost-Effectiveness of Community Screening for Glaucoma in Rural India: A Decision Analytical Model. Public Health, 155, 142-151. [Google Scholar] [CrossRef] [PubMed]
[25] Miller, S.E., Thapa, S., Robin, A.L., Niziol, L.M., Ramulu, P.Y., Woodward, M.A., et al. (2017) Glaucoma Screening in Nepal: Cup-to-Disc Estimate with Standard Mydriatic Fundus Camera Compared to Portable Nonmydriatic Camera. American Journal of Ophthalmology, 182, 99-106. [Google Scholar] [CrossRef] [PubMed]
[26] Upadhyaya, S., Agarwal, A., Rengaraj, V., Srinivasan, K., Newman Casey, P.A. and Schehlein, E. (2021) Validation of a Portable, Non-Mydriatic Fundus Camera Compared to Gold Standard Dilated Fundus Examination Using Slit Lamp Biomicroscopy for Assessing the Optic Disc for Glaucoma. Eye, 36, 441-447. [Google Scholar] [CrossRef] [PubMed]
[27] Li, Z., He, Y., Keel, S., Meng, W., Chang, R.T. and He, M. (2018) Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125, 1199-1206. [Google Scholar] [CrossRef] [PubMed]
[28] Medeiros, F.A., Jammal, A.A. and Thompson, A.C. (2019) From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. Ophthalmology, 126, 513-521. [Google Scholar] [CrossRef] [PubMed]
[29] Mursch-Edlmayr, A.S., Ng, W.S., Diniz-Filho, A., Sousa, D.C., Arnould, L., Schlenker, M.B., et al. (2020) Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice. Translational Vision Science & Technology, 9, Article 55. [Google Scholar] [CrossRef] [PubMed]
[30] Muhammad, H., Fuchs, T.J., De Cuir, N., De Moraes, C.G., Blumberg, D.M., Liebmann, J.M., et al. (2017) Hybrid Deep Learning on Single Wide-Field Optical Coherence Tomography Scans Accurately Classifies Glaucoma Suspects. Journal of Glaucoma, 26, 1086-1094. [Google Scholar] [CrossRef] [PubMed]
[31] Li, F., Wang, Z., Qu, G., Song, D., Yuan, Y., Xu, Y., et al. (2018) Automatic Differentiation of Glaucoma Visual Field from Non-Glaucoma Visual Filed Using Deep Convolutional Neural Network. BMC Medical Imaging, 18, Article No. 35. [Google Scholar] [CrossRef] [PubMed]
[32] Yousefi, S., Elze, T., Pasquale, L.R., Saeedi, O., Wang, M., Shen, L.Q., et al. (2020) Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology, 127, 1170-1178. [Google Scholar] [CrossRef] [PubMed]
[33] Kazemian, P., Lavieri, M.S., Van Oyen, M.P., Andrews, C. and Stein, J.D. (2018) Personalized Prediction of Glaucoma Progression under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods. Ophthalmology, 125, 569-577. [Google Scholar] [CrossRef] [PubMed]
[34] Salazar, D., Morales, E., Rabiolo, A., Capistrano, V., Lin, M., Afifi, A.A., et al. (2020) Pointwise Methods to Measure Long-Term Visual Field Progression in Glaucoma. JAMA Ophthalmology, 138, 536-543. [Google Scholar] [CrossRef] [PubMed]
[35] 张秀兰, 李飞. 人工智能和青光眼: 机遇与挑战[J]. 中华实验眼科杂志, 2018, 36(4): 245-247.
[36] Keel, S., Lee, P.Y., Scheetz, J., Li, Z., Kotowicz, M.A., MacIsaac, R.J., et al. (2018) Feasibility and Patient Acceptability of a Novel Artificial Intelligence-Based Screening Model for Diabetic Retinopathy at Endocrinology Outpatient Services: A Pilot Study. Scientific Reports, 8, Article No. 4330. [Google Scholar] [CrossRef] [PubMed]

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