切换至 "中华医学电子期刊资源库"

中华眼科医学杂志(电子版) ›› 2023, Vol. 13 ›› Issue (01) : 60 -64. doi: 10.3877/cma.j.issn.2095-2007.2023.01.012

综述

人工智能视力筛查在近视眼防控中的应用研究与展望
宗晨曦, 肖林, 宋红欣()   
  1. 102600 首都医科大学第四临床医学院2019级本科生
    102600 首都医科大学第四临床医学院眼视光医学教研室
    100730 首都医科大学附属北京同仁医院 北京同仁眼科中心 北京市眼科研究所 北京市眼科学与视觉科学重点实验室
  • 收稿日期:2022-07-03 出版日期:2023-02-28
  • 通信作者: 宋红欣
  • 基金资助:
    首都卫生发展科研专项基金项目(首发2022-1G-4083)

Advances and prospects in the application of artificial intelligence vision screening in the prevention and control of myopia

Chenxi Zong, Lin Xiao, Hongxin Song()   

  1. Undergraduate degree 2019, Capital Medical University Forth Clinical School, Beijing 102600, China
    Teaching and Research Section Ophthalmology of Capital Medical University Forth Clinical School, Beijing 102600, China
    Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing 100730, China
  • Received:2022-07-03 Published:2023-02-28
  • Corresponding author: Hongxin Song
引用本文:

宗晨曦, 肖林, 宋红欣. 人工智能视力筛查在近视眼防控中的应用研究与展望[J/OL]. 中华眼科医学杂志(电子版), 2023, 13(01): 60-64.

Chenxi Zong, Lin Xiao, Hongxin Song. Advances and prospects in the application of artificial intelligence vision screening in the prevention and control of myopia[J/OL]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2023, 13(01): 60-64.

近年来,我国青少年儿童近视率居高不下,学校视力筛查成为近视眼防控的重点工作。人工智能(AI)正在给医疗领域带来巨大改变。近年来,随着AI技术的快速发展,AI在眼科领域的应用逐步扩大,有望助力大规模的屈光检测和近视预测。本文中笔者介绍了近视眼防控领域AI视力筛查系统的应用现状,分析了AI系统目前存在的不足以及在真实世界里应用的障碍,最后展望了AI系统在近视眼防控中广泛的应用前景。

In recent years, the prevalence of myopia in children and adolescents has continued to rise in China. School-based vision screening has been the key work of myopia prevention and control. Artificial intelligence (AI) technology is bringing a great shift in healthcare. The application of AI has been expandingto ophthalmology, with the rapid progress of AI technology, including mass refraction and prediction of myopia. The current status of AI applications in screening and prediction of myopia was introduced and the current deficiencies of AI and hurdles for real-life deployment of AI and the application prospects of it were reviewed.

表1 人工智能视力筛查技术良好表现的文献汇总
[47]
Obermeyer Z, Emanuel EJ. Predicting the Future-Big Data, Machine Learning, and Clinical Medicine[J]. N Engl J Med, 2016, 375(13): 1216-1219.
[48]
Dutt S, Sivaraman A, Savoy F, et al. Insights into the growing popularity of artificial intelligence in ophthalmology[J]. Indian J Ophthalmol, 2020, 68(7): 1339-1346.
[49]
Foo LL, Ang M, Wong CW, et al. Is artificial intelligence a solution to the myopia pandemic?[J]. Br J Ophthalmol, 2021, 105(6): 741-744.
[50]
Yang X, Chen G, Qian Y, et al. Prediction of Myopia in Adolescents through Machine Learning Methods[J]. Int J Environ Res Public Health, 2020, 17(2):463.
[51]
唐涛,范玉琢,徐琼,等. 机器学习对青少年近视眼轴增长与近视度数增加关联性的预测作用[J]. 中华实验眼科杂志202038(2):134-139.
[52]
王妍茜,王成虎,张竞月,等. 人工智能应用于眼科的积极作用及其伦理问题[J]. 国际眼科杂志202222(6):1020-1024.
[53]
Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study[J]. J Med Internet Res, 2021, 23(11): e25856.
[54]
周楠,魏文斌. 大数据背景下的医学研究模式及其在眼科的应用[J]. 中华实验眼科杂志201937(12):1024-1028.
[55]
Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology[J]. Br J Ophthalmol, 2019, 103(2): 167-175.
[56]
林浩添,刘奕志. 我国眼科电子病历系统的专科功能要求及展望[J]. 中华眼科杂志201450(6):456-458.
[57]
Wu PC, Huang HM, Yu HJ, et al. Epidemiology of Myopia[J]. Asia Pac J Ophthalmol (Phila), 2016, 5(6): 386-393.
[1]
魏文斌,董力. 病理性近视是近视防控的重点和难点[J]. 安徽医科大学学报202257(2):169-172.
[2]
Ikuno Y. Overview of the complications of high myopia[J]. Retina, 2017, 37(12): 2347-2351.
[3]
徐捷,徐亮. 近视防控的六维度评估及防控模式[J]. 中华眼视光学与视觉科学杂志201820(3):129-132.
[4]
Flitcroft DI, He M, Jonas JB, et al. IMI-Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies[J]. Invest Ophthalmol Vis Sci, 2019, 60(3): M20-M30.
[5]
姚瑶,陈伟伟,付晶,等. Lea Symbols电子视力表与灯箱视力表在儿童视力检查中的对比研究[J]. 中国斜视与小儿眼科杂志202230(1):11-14,37.
[6]
娄斌,陈志钧,刘虎. 新版美国眼科学会《儿童眼评估临床指南》解读[J]. 中国斜视与小儿眼科杂志201927(3):37-38,34.
[7]
王春波,梁景斌,王海涛,等. 2847名2~6岁儿童主观、客观视力筛查结果分析[J]. 中国妇幼保健200722(21):2947-2948.
[8]
Salcido AAV, Bradley J, Donahue SP. Predictive value of photoscreening and traditional screening of preschool children[J]. JAAPOS, 2005, 9(2): 114-120.
[9]
李影,廖娅,刘一如,等. Spot双目视力筛查仪和自动电脑验光仪在近视筛查中结果的比较[J]. 中华眼视光学与视觉科学杂志202123(1):54-58.
[10]
刘国仿,边德换. 手持式视力筛查仪和自动电脑验光仪在青少年屈光检查中的应用[J]. 中国医疗器械信息202228(5):114-116.
[11]
Zhang X, Wang J, Li Y, et al. Diagnostic test accuracy of Spot and Plusoptix photoscreeners in detecting amblyogenic risk factors in children: a systemic review and meta-analysis[J]. Ophthalmic Physiol Opt, 2019, 39(4): 260-271.
[12]
马瑀涵,王路,夏志伟,等. 北京市学生标化与非标化近视检出率比较[J]. 中国学校卫生202142(2):195-197,202.
[13]
唐敏华,赵根明,姜永根,等. 上海市松江区2036名儿童青少年视力健康现状及其影响因素分析[J].中国儿童保健杂志202230(3):319-324.
[14]
郑冬冬,蒋丹丹,杨汉喜,等. 温州市一至三年级小学生近视及矫正现况[J]. 中国学校卫生201637(3):459-461.
[15]
许馨文,周巧玲,梁艳哲,等. 甘肃省儿童青少年近视筛查现况分析[J]. 中国初级卫生保健202236(2):92-94.
[16]
唐文婷,李佳倩,李世贝,等. 2018~2020年成都市新都区小学生近视流行病学研究[J]. 国际眼科杂志202222(1):148-152.
[17]
Collins ME, Guo X, Repka MX, et al. Lessons Learned From School-Based Delivery of Vision Care in Baltimore, Maryland[J]. Asia-Pac J Ophthalmol, 2022, 11(1): 6-11.
[18]
Seet B, Wong TY, Tan DT, et al. Myopia in Singapore: taking a public health approach[J]. Br J Ophthalmol, 2001, 85(5): 521-526.
[19]
Karuppiah V, Wong L, Tay V, et al. School-based programme to address childhood myopia in Singapore[J]. Singapore Med J, 2021, 62(2): 63-68.
[20]
Lin S, Ma Y, He X, et al. Using Decision Curve Analysis to Evaluate Common Strategies for Myopia Screening in School-Aged Children[J]. Ophthalmic Epidemiol, 2019, 26(4): 286-294.
[21]
赵原原,龚洁,杨莉华,等. AL/CR及裸眼视力筛查儿童青少年近视的准确性研究[J]. 现代预防医学201946(6):1028-1030.
[22]
He X, Zou H, Lu L, et al. Axial length/corneal radius ratio: association with refractive state and role on myopia detection combined with visual acuity in Chinese schoolchildren[J]. PLoS One, 2015, 10(2): e0111766.
[23]
Wang J, Xie H, Morgan I, et al. How to Conduct School Myopia Screening: Comparison Among Myopia Screening Tests and Determination of Associated Cutoffs[J]. Asia Pac J Ophthalmol (Phila), 2022, 11(1): 12-18.
[24]
刘红卫. 不同场所对小学生视力检测结果准确性的影响探究[J]. 基层医学论坛201317(10):1288-1289.
[25]
陈秀荣,王艳红,石慧,等. 儿童视力检查准确度的影响因素及护理对策[J]. 护理实践与研究20129(24):70-71.
[26]
林浩添,李龙辉,陈睛晶. 儿童眼病的人工智能研究进展[J]. 山东大学学报(医学版)202058(11):11-16.
[27]
林浩添,林铎儒. 我国未成年人视觉损害人工智能诊疗研究的现状和建议[J]. 中国临床新医学202013(2):111-114.
[28]
Mathers M, Keyes M, Wright M. A review of the evidence on the effectiveness of children's vision screening[J]. Child Care Health Dev, 2010, 36(6): 756-780.
[29]
Milante RR, Guo X, Neitzel AJ, et al. Analysis of vision screening failures in a school-based vision program (2016-19)[J]. J AAPOS, 2021, 25(1): 29.e21-29.e27.
[30]
Yang Y, Li R, Lin D, et al. Automatic identification of myopia based on ocular appearance images using deep learning[J]. Ann Transl Med, 2020, 8(11): 705.
[31]
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4): 230-243.
[32]
Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine[J]. J Family Med Prim Care, 2019, 8(7): 2328-2331.
[33]
Varadarajan AV, Poplin R, Blumer K, et al. Deep Learning for Predicting Refractive Error From Retinal Fundus Images[J]. Invest Ophthalmol Vis Sci, 2018, 59(7): 2861-2868.
[34]
Lin H, Long E, Ding X, et al. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study[J]. PLoS Med, 2018, 15(11): e1002674.
[35]
周云帆,陈娄,蒋沁. 眼健康大数据平台实践研究[J]. 医学信息学杂志201940(5):32-35.
[36]
宋倩,唐世琪. 互联网+视觉健康管理-武汉市近视眼预测预防系统在视觉健康管理中应用的探索与实践[J]. 中国临床保健杂志201922(1):142-144.
[37]
Foo LL, Ng WY, Lim GYS, et al. Artificial intelligence in myopia: current and future trends[J]. Curr Opin Ophthalmol, 2021, 32(5): 413-424.
[38]
Wu X, Liu L, Zhao L, et al. Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization[J]. Ann Transl Med, 2020, 8(11): 714.
[39]
田娟秀,刘国才,谷珊珊,等. 医学图像分析深度学习方法研究与挑战[J]. 自动化学报201844(3):401-424.
[40]
De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nat Med, 2018, 24(9): 1342-1350.
[41]
Ting DSW, Cheung CY, Lim G, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes[J]. JAMA, 2017, 318(22): 2211-2223.
[42]
Abràmoff MD, Lavin PT, Birch M, et al. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices[J]. NPJ Digit Med, 2018, 1: 39.
[43]
Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs[J]. JAMA, 2016, 316(22): 2402-2410.
[44]
Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29.
[45]
Tan TE, Anees A, Chen C, et al. Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study[J]. Lancet Digit Health, 2021, 3(5): e317-e329.
[46]
项毅帆,陈睛晶,胡伟玲,等. 基于移动终端的视力智能检查和管理系统的研发及临床应用价值评估[J]. 中华实验眼科杂志202139(9):798-802.
[1] 李洋, 蔡金玉, 党晓智, 常婉英, 巨艳, 高毅, 宋宏萍. 基于深度学习的乳腺超声应变弹性图像生成模型的应用研究[J/OL]. 中华医学超声杂志(电子版), 2024, 21(06): 563-570.
[2] 杨敬武, 周美君, 陈雨凡, 李素淑, 何燕妮, 崔楠, 刘红梅. 人工智能超声结合品管圈活动对低年资超声医师甲状腺结节风险评估能力的作用[J/OL]. 中华医学超声杂志(电子版), 2024, 21(05): 522-526.
[3] 明昊, 肖迎聪, 巨艳, 宋宏萍. 乳腺癌风险预测模型的研究现状[J/OL]. 中华乳腺病杂志(电子版), 2024, 18(05): 287-291.
[4] 叶莉, 杜宇. 深度学习在牙髓根尖周病临床诊疗中的应用[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(06): 351-356.
[5] 张茜柳, 余东升. 医源性牙外伤的发生原因和防治策略[J/OL]. 中华口腔医学研究杂志(电子版), 2024, 18(05): 287-294.
[6] 熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.
[7] 李伟, 宋子健, 赖衍成, 周睿, 吴涵, 邓龙昕, 陈锐. 人工智能应用于前列腺癌患者预后预测的研究现状及展望[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 541-546.
[8] 黄俊龙, 李文双, 李晓阳, 刘柏隆, 陈逸龙, 丘惠平, 周祥福. 基于盆底彩超的人工智能模型在女性压力性尿失禁分度诊断中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 597-605.
[9] 莫淇舟, 苏劲, 黄健, 李健维, 李思宁, 柳建军. 智能控压输尿管软镜碎石吸引取石术在直径10~25 mm上尿路结石中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(05): 497-502.
[10] 苏博兴, 肖博, 李建兴. 2024年美国泌尿外科学会年会结石领域手术治疗相关热点研究及解读[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 303-308.
[11] 莫林键, 杨舒博, 农卫赟, 程继文. 人工智能虚拟数字医师在钬激光前列腺剜除日间手术患教管理中的应用[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(04): 318-322.
[12] 李义亮, 苏拉依曼·牙库甫, 麦麦提艾力·麦麦提明, 克力木·阿不都热依木. 机器人与腹腔镜食管裂孔疝修补术联合Nissen 胃底折叠术短期疗效分析[J/OL]. 中华疝和腹壁外科杂志(电子版), 2024, 18(05): 512-517.
[13] 王洪, 王骏华, 范建楠. 人工智能技术在肩袖损伤中的研究进展[J/OL]. 中华肩肘外科电子杂志, 2024, 12(04): 356-361.
[14] 孙铭远, 褚恒, 徐海滨, 张哲. 人工智能应用于多发性肺结节诊断的研究进展[J/OL]. 中华临床医师杂志(电子版), 2024, 18(08): 785-790.
[15] 杨松林, 黄仕豪, 王丽珠, 李禧萌, 邹飞翔, 李坤炜, 梁明柱, 陈炳辉. 良性肺结节生长变化的影像学评价[J/OL]. 中华介入放射学电子杂志, 2024, 12(04): 344-350.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?