The standardized design and application guidelines:a primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography_Chinese Journal of Ocular Fundus Diseases

Authors：

 Ocular Fundus Diseases Group of Chinese Ophthalmological Society ,  Expert Group for Artificial Intelligence Research, Development, and Application

Corresponding author: Li Xiaoxin, Email: dr_lixiaoxin@163.com; Zhang Ming, Email: zhangmingscu@126.com; Xu Yangwu, Email: ywxu@ieee.org;

Corresponding author：

Keywords：

Guidebooks; Artificial intelligence; Macula lutea; Fundus lesions; Screening

DOI：

10.3760/cma.j.cn511434-20220616-00364

Video：

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With the popularity and development of artificial intelligence (AI), disease screening systems based on AI algorithms are gradually emerging in the medical field. Such systems can be used for primary screening of diseases to relieve the pressure on primary health care. In recent years, AI algorithms have demonstrated good performance in the analysis and identification of lesion signs in the macular region of fundus color photography, and a screening system for fundus lesion signs applicable to primary screening is bound to emerge in the future. Therefore, to standardize the design and clinical application of the macular region lesion sign screening systems based on AI algorithms, the Ocular Fundus Diseases Group of Chinese Ophthalmological Society, in collaboration with relevant experts, has developed this guideline after investigating issues, discussing production evidence, and holding guideline workshops. This guideline aims to establish uniform standards for the definition of the macular region and lesion signs, AI adoption scenarios, algorithm model construction, datasets establishment and labeling, architecture and functions design, and image data acquisition for the screening system to guide the implementation of the screening work.

Citation： Ocular Fundus Diseases Group of Chinese Ophthalmological Society, Expert Group for Artificial Intelligence Research, Development, and Application. The standardized design and application guidelines:a primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography. Chinese Journal of Ocular Fundus Diseases, 2022, 38(9): 711-728. doi: 10.3760/cma.j.cn511434-20220616-00364 Copy

1.	陈浩宇. 糖尿病视网膜病变筛查手册诊断和处理(第2版)[M]. 北京: 人民卫生出版社, 2016: 10-12.Chen HY. Handbook of retinal screening in diabetes diagnosis & management (2 nd ed)[M]. Beijing: People' Medical Publishing House, 2016: 10-12.
2.	沙文茹. 《中国眼健康白皮书》发布: 致盲性眼病有效遏制[J]. 中国医药科学, 2020, 10(13): 3. DOI: 10.3969/j.issn.2095-0616.2020.13.002.Sha WR. “China Eye Health White Paper” released: effectively inhibition of blinding eye diseases[J]. China Medicine and Pharmacy, 2020, 10(13): 3. DOI: 10.3969/j.issn.2095-0616.2020.13.002.
3.	Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology[J]. Asia Pac J Ophthalmol (Phila), 2019, 8(3): 264-272. DOI: 10.22608/APO.2018479.
4.	Orlando JI, Fu H, Breda JB, et al. Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J/OL]. Med Image Anal, 2020, 59: 101570[2019-10-08].　https://pubmed.ncbi.nlm.nih.gov/31630011/. DOI: 10.1016/j.media.2019.101570.
5.	吴乐正, 林顺潮. 临床眼黄斑病学[M]. 北京: 北京科学技术出版社, 2007: 29-104.Wu LZ, Lin SC. Clinical maculopathy[M]. Beijing: Beijing Science and Technology Publishing, 2007: 29-104.
6.	Esteva A, Kuprel B, Novoa RA, et al. Correction: corrigendum: dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 546(7660): 686. DOI: 10.1038/nature22985.
7.	Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318(22): 2199-2210. DOI: 10.1001/jama.2017.14585.
8.	Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 2017, 124(7): 962-969. DOI: 10.1016/j.ophtha.2017.02.008.
9.	中华医学会眼科学分会青光眼学组, 中国医学装备协会眼科人工智能学组. 中国基于眼底照相的人工智能青光眼辅助筛查系统规范化设计及应用指南(2020年)[J]. 中华眼科杂志, 2020, 56(6): 423-432. DOI: 10.3760/cma.j.cn112142-20200102-00003.Chinese Medical Association Ophthalmology Branch Glaucoma Group, China Medical Equipment Association Ophthalmology Artificial Intelligence Group. Guidelines for standardized design and application of artificial intelligence glaucoma auxiliary screening system based on fundus photography in China (2020)[J]. Chin J Ophthalmol, 2020, 56(6): 423-432. DOI: 10.3760/cma.j.cn112142-20200102-00003.
10.	袁进, 雷博, 张明, 等. 基于眼底照相的糖尿病视网膜病变人工智能筛查系统应用指南[J]. 中华实验眼科杂志, 2019, 37(8): 593-598. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.001.Yuan J, Lei B, Zhang M, et al. Guidelines for artificial intelligent diabetic retinopathy screening system based on fundus photography[J]. Chin J Exp Ophthalmol, 2019, 37(8): 593-598. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.001.
11.	中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第1部分: 术语. YY/T 1833.1-2022[S/OL]. (2020-07-03)[2022-06-16]. http://www.cnpharm.com/upload/resources/file/2020/07/03/54879.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 1: Terminology. YY/T 1833.1-2022[S/OL]. (2020-07-03)[2022-06-16]. http://www.cnpharm.com/upload/resources/file/2020/07/03/54879.pdf.
12.	中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第2部分: 数据集通用要求. YY/T 1833.2-2022[S/OL]. (2020-07-03)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/infoAttach/38b8b027-8b43-43c1-b744-c0ac891b5ec8.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 2: General requirements for datasets. YY/T 1833.2-2022[S/OL]. (2020-07-03) [2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/infoAttach/38b8b027-8b43-43c1-b744-c0ac891b5ec8.pdf.
13.	中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第3部分: 数据标注通用要求(征求意见稿)[S/OL](2021-06-04)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/images/2021080213295600571.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 2: General requirements for data annotation (draft for comments)[S/OL]. (2021-06-04)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/images/2021080213295600571.pdf.
14.	中国质量检验协会. 眼底彩照标注与质量控制规范(T/CAQI 166-2020)[J]. 中华实验眼科杂志, 2021, 39(9): 761-768. DOI: 10.3760/cma.j.cn115989-20210106-00013.China Association for Quality Inspection. Annotation and quality control specifications for fundus color photographs (T/CAQI 166-2020)[J]. Chin J Exp Ophthalmol, 2021, 39(9): 761-768. DOI: 10.3760/cma.j.cn115989-20210106-00013.
15.	Davis MD, Gangnon RE, Lee LY, et al. The age-related eye disease study severity scale for age-related macular degeneration: AREDS report no. 17[J]. Arch Ophthalmol, 2005, 123(11): 1484-1498. DOI: 10.1001/archopht.123.11.1484.
16.	Britton G, Liaaenjensen S, Hanspeter P. Carotenoids Volume 5: nutrition and health[J]. Carotenoids, 2009, 24(10): 497-522.
17.	美国眼科学会, 赵家良. 眼科临床指南[M]. 北京: 人民卫生出版社, 2013: 57-59.American Academy of Ophthalmology, Zhao JL. Preferred practice pattern[M]. Beijing: People' Medical Publishing House, 2013: 57-59.
18.	张承芬. 眼底病学(第2版)[M]. 北京: 人民卫生出版社, 2010: 397-398.Zhang CF. Diseases of ocular fundus (2 nd ed)[M]. Beijing: People' Medical Publishing House, 2010: 397-398.
19.	杨依柳, 杨婷婷, 陆方, 等. 新生血管性老年性黄斑变性亚型报告的国际新命名专家共识解读[J]. 中华眼底病杂志, 2022, 38(2): 99-107. DOI: 10.3760/cma.j.cn511434-20220128-00055.Yang YL, Yang TT, Lu F, et al. Brief interpretation of the consensus nomenclature for reporting neovascular age-related macular degeneration data[J]. Chin J Ocul Fundus Dis, 2022, 38(2): 99-107. DOI: 10.3760/cma.j.cn511434-20220128-00055.
20.	中华医学会眼科学分会眼底病学组. 中国老年性黄斑变性临床诊断治疗路径[J]. 中华眼底病杂志, 2013, 29(4): 343-355. DOI: 10.3760/cma.j.issn.1005-1015.2013.04.002.Fundus Disease Group of Chinese Medical Association Ophthalmology Branch. Clinical pathway of age-related macular degeneration in China[J]. Chin J Ocul Fundus Dis, 2013, 29(4): 343-355. DOI: 10.3760/cma.j.issn.1005-1015.2013.04.002.
21.	文峰, 张雄泽. 提高对视网膜出血的分类及临床意义的认识[J]. 眼科, 2009, 18(4): 221-224.Wen F, Zhang XZ. Classification and clinical significance of retinal hemorrhage[J]. Ophthalmol CHN, 2009, 18(4): 221-224.
22.	Sarks J, Tang K, Killingsworth M, et al. Development of atrophy of the retinal pigment epithelium around disciform scars[J]. Br J Ophthalmol, 2006, 90(4): 442-446. DOI: 10.1136/bjo.2005.083022.
23.	Gass JD. Reappraisal of biomicroscopic classification of stages of development of a macular hole[J]. Am J Ophthalmol, 1995, 119(6): 752-759. DOI: 10.1016/s0002-9394(14)72781-3.
24.	张惠蓉, 王薇. 特发性黄斑视网膜前膜[J]. 中国实用眼科杂志, 1997, 15(10): 4.Zhang HR, Wang W. Idiopathic preretinal macular membrane[J]. Chin J Pract Ophthalmol, 1997, 15(10): 4.
25.	Early treatment diabetic retinopathy study design and baseline patient characteristics: ETDRS report number 7[J]. Ophthalmology, 1991, 98(5): 741-756. DOI: 10.1016/s0161-6420(13)38009-9.
26.	严密. 黄斑囊样水肿[J]. 中华眼底病杂志, 2002, 18(3): 234-235. DOI: 3760/j. issn: 1005-1015.2002. 03.035.Yan M. Cystoid macular edema[J]. Chin J Ocul Fundus Dis, 2002, 18(3): 234-235. DOI: 10.3760/j.issn:1005-1015.2002.03.035.
27.	Sigler EJ, Randolph JC, Kiernan DF. Longitudinal analysis of the structural pattern of pseudophakic cystoid macular edema using multimodal imaging[J]. Graefe's Arch Clin Exp Ophthalmol, 2016, 254(1): 43-51. DOI: 10.1007/s00417-015-3000-8.
28.	Brinton DA, Wilkinson CP. 视网膜脱离: 原理与实践(第3版)[M]. 马凯, 杨庆松, 徐军, 译. 2版. 北京: 人民卫生出版社, 2011: 65-67.Brinton DA, Wilkinson CP. Retinal detachment: principles and practice (3rd ed)[M]. Ma K, Yang QS, Xu J, translation. 2nd ed. Beijing: People' Medical Publishing House, 2011: 65-67.
29.	Zayit-Soudry S, Moroz I, Loewenstein A. Retinal pigment epithelial detachment[J]. Surv Ophthalmol, 2007, 52(3): 227-243. DOI: 10.1016/j.survophthal.2007.02.008.
30.	Madjarov G, Kocev D, Gjorgjevikj D, et al. An extensive experimental comparison of methods for multi-label learning[J]. Pattern Recogn, 2012, 45(9): 3084-3104. DOI: 10.1016/j.patcog.2012.03.004.
31.	Landis JR, Koch GG. The measurement of observer agreement for categorical data[J]. Biometrics, 1977, 33(1): 159-174. DOI: 10.2307/2529310.
32.	Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: a survey[J]. Int J Comput Vision, 2020, 128(2): 261-318. DOI: 10.1007/s11263-019-01247-4.
33.	Li T, Bo W, Hu C, et al. Applications of deep learning in fundus images: a review[J/OL]. Med Image Anal, 2021, 69: 101971[2021-01-20].https://pubmed.ncbi.nlm.nih.gov/33524824/. DOI: 10.1016/j.media.2021.101971.
34.	Zhang YJ. A survey on evaluation methods for image segmentation[J]. Pattern Recogn, 1996, 29(8): 1335-1346. DOI: 10.1016/0031-3203(95)00169-7.
35.	李建军, 徐亮, 彭晓燕, 等. 远程眼科单张眼底像质量标准(征求意见稿)[J]. 眼科, 2015(1): 11-12. DOI: 10.13281/j.cnki.issn.1004-4469.2015.01.005.Li JJ, Xu L, Peng XY, et al. Quality standard for single fundus images in teleophthalmology (draft for comments)[J]. Ophthalmol CHN, 2015(1): 11-12. DOI: 10.13281/j.cnki.issn.1004-4469.2015.01.005.
36.	中华医学会眼科学分会眼底病学组, 中国医师协会眼科医师分会眼底病专业委员会. 我国糖尿病视网膜病变筛查的图像采集及阅片指南(2017年)[J]. 中华眼科杂志, 2017, 53(12): 890-896. DOI: 10.3760/cma.j.issn.0412-4081.2017.12.003.Fundus Disease Group of Ophthalmology Branch of Chinese Medical Association, Fundus Disease Professional Committee of Ophthalmologist Branch of Chinese Medical Doctor Association. Guidelines for image acquisition and reading of diabetic retinopathy screening in my country (2017)[J]. Chin J Ophthalmol, 2017, 53(12): 890-896. DOI: 10.3760/cma.j.issn.0412-4081.2017.12.003.
37.	周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 25.Zhou ZH. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 25.
38.	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. DOI: 10.1001/jama.2016.17216.
39.	国家药品监督管理局医疗器械技术审评中心. 人工智能医疗器械注册审查指导原则 [EB/OL]. (2022-03-09)[2022-06-16]. https://www.cmde.org.cn/flfg/zdyz/zdyzwbk/20220309091014461.html.Center for Medical Device Evalution. NMPA. Guidelines for the registration review of artificial intelligence medical devices[EB/OL]. (2022-03-09)[2022-06-16]. https://www.cmde.org.cn/flfg/zdyz/zdyzwbk/20220309091014461.html.
40.	邵志强. 抽样调查中样本容量的确定方法[J]. 统计与决策, 2012, 22: 12-14. DOI: 10.13546/j.cnki.tjyjc.2012.22.002.Shao ZQ. The method of determining sample size in a sampling survey [J]. Statistics & Decision. 2012, 22: 12-14. DOI: 10.13546/j.cnki.tjyjc.2012.22.002.
41.	国家食品药品监督管理总局. 医疗器械临床试验质量管理规范[EB/OL]. (2021-09-28)[2022-06-16]. https://www.nmpa.gov.cn/xxgk/fgwj/bmgzh/20160323141701747.html?type=pc&m=.National Medical Products Administration. Practice for quality management of clinical trials of medical devices[EB/OL]. (2021-09-28)[2022-06-16].https://www.nmpa.gov.cn/xxgk/fgwj/bmgzh/20160323141701747.html?type=pc&m=.
42.	国家药品监督管理局医疗器械技术审评中心. 深度学习辅助决策医疗器械软件审评要点及相关说明[EB/OL]. (2021-09-28)[2022-06-16]. https://www.cmde.org.cn/xwdt/zxyw/20190628151300923.html.Center for Medical Device Evalution. NMPA. Key points of deep learning assisted decision making medical device software review and related description. [EB/OL]. (2021-09-28)[2022-06-16]. https://www.cmde.org.cn/xwdt/zxyw/20190628151300923.html.
43.	曹葭, 姚勇, 傅东红, 等. 无锡市50岁及以上人群年龄相关性黄斑变性流行病学调查[J]. 中国实用眼科杂志, 2013, 31(4): 494-498. DOI: 3760/cma. j. issn. 1006-4443.2013. 04.032.Cao J, Yao Y, Fu DH, et al. Prevalence study of age-related macular degeneration over the age of 50's in Wuxi[J]. Chin J Pract Ophthalmol, 2013, 31(4): 494-498. DOI: 10.3760/cma.j.issn.1006-4443.2013.04.032.
44.	杨倩, 韩毳, 刘宁, 等. 免散瞳数码眼底照相在眼底黄斑部疾病筛查中的应用[J]. 眼科新进展, 2012, 32(5): 473-475. DOI: 10.13389/j.cnki.rao.2012.05.010.Yang Q, Han C, Liu N, et al. Application of non-mydriatic digital fundus photography in fundus macular disease screening[J]. Rec Adv Ophthalmol, 2012, 32(5): 473-475. DOI: 10.13389/j.cnki.rao.2012.05.010.
45.	Jonas JB, Xu L, Wang YX. The Beijing eye study[J]. Acta ophthalmologica, 2009, 87(3): 247-261. DOI: 10.1111/j.1755-3768.2008.01385.x.
46.	Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning[J]. Big Data, 2019, 6(1): 1-48. DOI: 10.1186/s40537-019-0197-0.
47.	Padilla R, Netto SL, Da Silva EA. A survey on performance metrics for object-detection algorithms[C]. 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Wuhan, 2020: 237-242.
48.	Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
49.	中华医学会健康管理学分会, 《中华健康管理学杂志》编辑委员会. 健康体检主检报告撰写专家共识[J]. 中华健康管理学杂志, 2020, 14(1): 8-11. DOI: 10.3760/cma.j.issn.1674-0815.2020.01.003.Chinese Society of Health Management, The Editorial Board of Chinese Journal of Heatlth Management. Expert consensus on the chief physician report for health checkup[J]. Chin J Health Manage, 2020, 14(1): 8-11. DOI: 10.3760/cma.j.issn.1674-0815.2020.01.003.
50.	国家食品药品监督管理总局. 中华人民共和国医药行业标准: 眼科仪器眼底照相机[S]. 北京: 中国标准出版社, 2008.National Medical Products Administration. People's Republic of China Pharmaceutical Industry Standard: ophthalmic instruments fundus camera[S]. Beijing: China Standards Press, 2008.
51.	宋琳琳, 李志清, 马文江, 等. 运用云医疗远程平台构建糖尿病视网膜病变分级诊疗模式[J]. 现代医院管理, 2020, 18(3): 17-20. DOI: 10.3969/j.issn.1672-4232.2020.03.005.Song LL, Li ZQ, Ma WJ, et al. Establishment of a hierarchical diagnosis and treatment model for diabetic retinopathy by cloud medical remote platform[J]. Modern Hospital Management, 2020, 18(3): 17-20. DOI: 10.3969/j.issn.1672-4232.2020.03.005.
52.	谭丹华, 万里飞, 邹溢辉. 医院信息系统HIS和及其应用[J]. 中国医疗器械信息, 2007, 13(9): 39-40. DOI: 10.3969/j.issn.1006-6586.2007.09.011.Tan DH, Wan LF, Zou YH. Hospital information system and its application[J]. China Medical Device Information, 2007, 13(9): 39-40. DOI: 10.3969/j.issn.1006-6586.2007.09.011.
53.	李贵祥, 刘国祥, 李刚荣, 等. 大型综合性医院 PACS 系统设计与实施[J]. 中国医院管理, 2005, 25(1): 46-48. DOI: 10.3969/j.issn.1001-5329.2005.01.019.Li GX, Liu GX, Li GR, et al. The design and implementation of PACS system in large-scale comprehensive hospitals[J]. Chinese Hospital Management, 2005, 25(1): 46-48. DOI: 10.3969/j.issn.1001-5329.2005.01.019.
54.	Long M, Zhu H, Wang J, et al. Unsupervised domain adaptation with residual transfer networks[C]. 30th International Conference on Neural Information Processing Systems, New York: Curran Associates Inc. , 2016: 136-144.
55.	Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]. The 32 nd International Conference on Machine Learning, Lille, 2015: 1180-1189.

1. 陈浩宇. 糖尿病视网膜病变筛查手册诊断和处理(第2版)[M]. 北京: 人民卫生出版社, 2016: 10-12.Chen HY. Handbook of retinal screening in diabetes diagnosis & management (2 nd ed)[M]. Beijing: People' Medical Publishing House, 2016: 10-12.
2. 沙文茹. 《中国眼健康白皮书》发布: 致盲性眼病有效遏制[J]. 中国医药科学, 2020, 10(13): 3. DOI: 10.3969/j.issn.2095-0616.2020.13.002.Sha WR. “China Eye Health White Paper” released: effectively inhibition of blinding eye diseases[J]. China Medicine and Pharmacy, 2020, 10(13): 3. DOI: 10.3969/j.issn.2095-0616.2020.13.002.
3. Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology[J]. Asia Pac J Ophthalmol (Phila), 2019, 8(3): 264-272. DOI: 10.22608/APO.2018479.
4. Orlando JI, Fu H, Breda JB, et al. Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J/OL]. Med Image Anal, 2020, 59: 101570[2019-10-08].　https://pubmed.ncbi.nlm.nih.gov/31630011/. DOI: 10.1016/j.media.2019.101570.
5. 吴乐正, 林顺潮. 临床眼黄斑病学[M]. 北京: 北京科学技术出版社, 2007: 29-104.Wu LZ, Lin SC. Clinical maculopathy[M]. Beijing: Beijing Science and Technology Publishing, 2007: 29-104.
6. Esteva A, Kuprel B, Novoa RA, et al. Correction: corrigendum: dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 546(7660): 686. DOI: 10.1038/nature22985.
7. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer[J]. JAMA, 2017, 318(22): 2199-2210. DOI: 10.1001/jama.2017.14585.
8. Gargeya R, Leng T. Automated identification of diabetic retinopathy using deep learning[J]. Ophthalmology, 2017, 124(7): 962-969. DOI: 10.1016/j.ophtha.2017.02.008.
9. 中华医学会眼科学分会青光眼学组, 中国医学装备协会眼科人工智能学组. 中国基于眼底照相的人工智能青光眼辅助筛查系统规范化设计及应用指南(2020年)[J]. 中华眼科杂志, 2020, 56(6): 423-432. DOI: 10.3760/cma.j.cn112142-20200102-00003.Chinese Medical Association Ophthalmology Branch Glaucoma Group, China Medical Equipment Association Ophthalmology Artificial Intelligence Group. Guidelines for standardized design and application of artificial intelligence glaucoma auxiliary screening system based on fundus photography in China (2020)[J]. Chin J Ophthalmol, 2020, 56(6): 423-432. DOI: 10.3760/cma.j.cn112142-20200102-00003.
10. 袁进, 雷博, 张明, 等. 基于眼底照相的糖尿病视网膜病变人工智能筛查系统应用指南[J]. 中华实验眼科杂志, 2019, 37(8): 593-598. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.001.Yuan J, Lei B, Zhang M, et al. Guidelines for artificial intelligent diabetic retinopathy screening system based on fundus photography[J]. Chin J Exp Ophthalmol, 2019, 37(8): 593-598. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.001.
11. 中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第1部分: 术语. YY/T 1833.1-2022[S/OL]. (2020-07-03)[2022-06-16]. http://www.cnpharm.com/upload/resources/file/2020/07/03/54879.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 1: Terminology. YY/T 1833.1-2022[S/OL]. (2020-07-03)[2022-06-16]. http://www.cnpharm.com/upload/resources/file/2020/07/03/54879.pdf.
12. 中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第2部分: 数据集通用要求. YY/T 1833.2-2022[S/OL]. (2020-07-03)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/infoAttach/38b8b027-8b43-43c1-b744-c0ac891b5ec8.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 2: General requirements for datasets. YY/T 1833.2-2022[S/OL]. (2020-07-03) [2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/infoAttach/38b8b027-8b43-43c1-b744-c0ac891b5ec8.pdf.
13. 中国食品药品检定研究院. 人工智能医疗器械质量要求和评价第3部分: 数据标注通用要求(征求意见稿)[S/OL](2021-06-04)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/images/2021080213295600571.pdf.National Institutes for Food and Drug Control. Artificial intelligence medical device—Quality requirements and evaluation—Part 2: General requirements for data annotation (draft for comments)[S/OL]. (2021-06-04)[2022-06-16]. https://www.nifdc.org.cn/directory/web/nifdc/images/2021080213295600571.pdf.
14. 中国质量检验协会. 眼底彩照标注与质量控制规范(T/CAQI 166-2020)[J]. 中华实验眼科杂志, 2021, 39(9): 761-768. DOI: 10.3760/cma.j.cn115989-20210106-00013.China Association for Quality Inspection. Annotation and quality control specifications for fundus color photographs (T/CAQI 166-2020)[J]. Chin J Exp Ophthalmol, 2021, 39(9): 761-768. DOI: 10.3760/cma.j.cn115989-20210106-00013.
15. Davis MD, Gangnon RE, Lee LY, et al. The age-related eye disease study severity scale for age-related macular degeneration: AREDS report no. 17[J]. Arch Ophthalmol, 2005, 123(11): 1484-1498. DOI: 10.1001/archopht.123.11.1484.
16. Britton G, Liaaenjensen S, Hanspeter P. Carotenoids Volume 5: nutrition and health[J]. Carotenoids, 2009, 24(10): 497-522.
17. 美国眼科学会, 赵家良. 眼科临床指南[M]. 北京: 人民卫生出版社, 2013: 57-59.American Academy of Ophthalmology, Zhao JL. Preferred practice pattern[M]. Beijing: People' Medical Publishing House, 2013: 57-59.
18. 张承芬. 眼底病学(第2版)[M]. 北京: 人民卫生出版社, 2010: 397-398.Zhang CF. Diseases of ocular fundus (2 nd ed)[M]. Beijing: People' Medical Publishing House, 2010: 397-398.
19. 杨依柳, 杨婷婷, 陆方, 等. 新生血管性老年性黄斑变性亚型报告的国际新命名专家共识解读[J]. 中华眼底病杂志, 2022, 38(2): 99-107. DOI: 10.3760/cma.j.cn511434-20220128-00055.Yang YL, Yang TT, Lu F, et al. Brief interpretation of the consensus nomenclature for reporting neovascular age-related macular degeneration data[J]. Chin J Ocul Fundus Dis, 2022, 38(2): 99-107. DOI: 10.3760/cma.j.cn511434-20220128-00055.
20. 中华医学会眼科学分会眼底病学组. 中国老年性黄斑变性临床诊断治疗路径[J]. 中华眼底病杂志, 2013, 29(4): 343-355. DOI: 10.3760/cma.j.issn.1005-1015.2013.04.002.Fundus Disease Group of Chinese Medical Association Ophthalmology Branch. Clinical pathway of age-related macular degeneration in China[J]. Chin J Ocul Fundus Dis, 2013, 29(4): 343-355. DOI: 10.3760/cma.j.issn.1005-1015.2013.04.002.
21. 文峰, 张雄泽. 提高对视网膜出血的分类及临床意义的认识[J]. 眼科, 2009, 18(4): 221-224.Wen F, Zhang XZ. Classification and clinical significance of retinal hemorrhage[J]. Ophthalmol CHN, 2009, 18(4): 221-224.
22. Sarks J, Tang K, Killingsworth M, et al. Development of atrophy of the retinal pigment epithelium around disciform scars[J]. Br J Ophthalmol, 2006, 90(4): 442-446. DOI: 10.1136/bjo.2005.083022.
23. Gass JD. Reappraisal of biomicroscopic classification of stages of development of a macular hole[J]. Am J Ophthalmol, 1995, 119(6): 752-759. DOI: 10.1016/s0002-9394(14)72781-3.
24. 张惠蓉, 王薇. 特发性黄斑视网膜前膜[J]. 中国实用眼科杂志, 1997, 15(10): 4.Zhang HR, Wang W. Idiopathic preretinal macular membrane[J]. Chin J Pract Ophthalmol, 1997, 15(10): 4.
25. Early treatment diabetic retinopathy study design and baseline patient characteristics: ETDRS report number 7[J]. Ophthalmology, 1991, 98(5): 741-756. DOI: 10.1016/s0161-6420(13)38009-9.
26. 严密. 黄斑囊样水肿[J]. 中华眼底病杂志, 2002, 18(3): 234-235. DOI: 3760/j. issn: 1005-1015.2002. 03.035.Yan M. Cystoid macular edema[J]. Chin J Ocul Fundus Dis, 2002, 18(3): 234-235. DOI: 10.3760/j.issn:1005-1015.2002.03.035.
27. Sigler EJ, Randolph JC, Kiernan DF. Longitudinal analysis of the structural pattern of pseudophakic cystoid macular edema using multimodal imaging[J]. Graefe's Arch Clin Exp Ophthalmol, 2016, 254(1): 43-51. DOI: 10.1007/s00417-015-3000-8.
28. Brinton DA, Wilkinson CP. 视网膜脱离: 原理与实践(第3版)[M]. 马凯, 杨庆松, 徐军, 译. 2版. 北京: 人民卫生出版社, 2011: 65-67.Brinton DA, Wilkinson CP. Retinal detachment: principles and practice (3rd ed)[M]. Ma K, Yang QS, Xu J, translation. 2nd ed. Beijing: People' Medical Publishing House, 2011: 65-67.
29. Zayit-Soudry S, Moroz I, Loewenstein A. Retinal pigment epithelial detachment[J]. Surv Ophthalmol, 2007, 52(3): 227-243. DOI: 10.1016/j.survophthal.2007.02.008.
30. Madjarov G, Kocev D, Gjorgjevikj D, et al. An extensive experimental comparison of methods for multi-label learning[J]. Pattern Recogn, 2012, 45(9): 3084-3104. DOI: 10.1016/j.patcog.2012.03.004.
31. Landis JR, Koch GG. The measurement of observer agreement for categorical data[J]. Biometrics, 1977, 33(1): 159-174. DOI: 10.2307/2529310.
32. Liu L, Ouyang W, Wang X, et al. Deep learning for generic object detection: a survey[J]. Int J Comput Vision, 2020, 128(2): 261-318. DOI: 10.1007/s11263-019-01247-4.
33. Li T, Bo W, Hu C, et al. Applications of deep learning in fundus images: a review[J/OL]. Med Image Anal, 2021, 69: 101971[2021-01-20].https://pubmed.ncbi.nlm.nih.gov/33524824/. DOI: 10.1016/j.media.2021.101971.
34. Zhang YJ. A survey on evaluation methods for image segmentation[J]. Pattern Recogn, 1996, 29(8): 1335-1346. DOI: 10.1016/0031-3203(95)00169-7.
35. 李建军, 徐亮, 彭晓燕, 等. 远程眼科单张眼底像质量标准(征求意见稿)[J]. 眼科, 2015(1): 11-12. DOI: 10.13281/j.cnki.issn.1004-4469.2015.01.005.Li JJ, Xu L, Peng XY, et al. Quality standard for single fundus images in teleophthalmology (draft for comments)[J]. Ophthalmol CHN, 2015(1): 11-12. DOI: 10.13281/j.cnki.issn.1004-4469.2015.01.005.
36. 中华医学会眼科学分会眼底病学组, 中国医师协会眼科医师分会眼底病专业委员会. 我国糖尿病视网膜病变筛查的图像采集及阅片指南(2017年)[J]. 中华眼科杂志, 2017, 53(12): 890-896. DOI: 10.3760/cma.j.issn.0412-4081.2017.12.003.Fundus Disease Group of Ophthalmology Branch of Chinese Medical Association, Fundus Disease Professional Committee of Ophthalmologist Branch of Chinese Medical Doctor Association. Guidelines for image acquisition and reading of diabetic retinopathy screening in my country (2017)[J]. Chin J Ophthalmol, 2017, 53(12): 890-896. DOI: 10.3760/cma.j.issn.0412-4081.2017.12.003.
37. 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 25.Zhou ZH. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 25.
38. 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. DOI: 10.1001/jama.2016.17216.
39. 国家药品监督管理局医疗器械技术审评中心. 人工智能医疗器械注册审查指导原则 [EB/OL]. (2022-03-09)[2022-06-16]. https://www.cmde.org.cn/flfg/zdyz/zdyzwbk/20220309091014461.html.Center for Medical Device Evalution. NMPA. Guidelines for the registration review of artificial intelligence medical devices[EB/OL]. (2022-03-09)[2022-06-16]. https://www.cmde.org.cn/flfg/zdyz/zdyzwbk/20220309091014461.html.
40. 邵志强. 抽样调查中样本容量的确定方法[J]. 统计与决策, 2012, 22: 12-14. DOI: 10.13546/j.cnki.tjyjc.2012.22.002.Shao ZQ. The method of determining sample size in a sampling survey [J]. Statistics & Decision. 2012, 22: 12-14. DOI: 10.13546/j.cnki.tjyjc.2012.22.002.
41. 国家食品药品监督管理总局. 医疗器械临床试验质量管理规范[EB/OL]. (2021-09-28)[2022-06-16]. https://www.nmpa.gov.cn/xxgk/fgwj/bmgzh/20160323141701747.html?type=pc&m=.National Medical Products Administration. Practice for quality management of clinical trials of medical devices[EB/OL]. (2021-09-28)[2022-06-16].https://www.nmpa.gov.cn/xxgk/fgwj/bmgzh/20160323141701747.html?type=pc&m=.
42. 国家药品监督管理局医疗器械技术审评中心. 深度学习辅助决策医疗器械软件审评要点及相关说明[EB/OL]. (2021-09-28)[2022-06-16]. https://www.cmde.org.cn/xwdt/zxyw/20190628151300923.html.Center for Medical Device Evalution. NMPA. Key points of deep learning assisted decision making medical device software review and related description. [EB/OL]. (2021-09-28)[2022-06-16]. https://www.cmde.org.cn/xwdt/zxyw/20190628151300923.html.
43. 曹葭, 姚勇, 傅东红, 等. 无锡市50岁及以上人群年龄相关性黄斑变性流行病学调查[J]. 中国实用眼科杂志, 2013, 31(4): 494-498. DOI: 3760/cma. j. issn. 1006-4443.2013. 04.032.Cao J, Yao Y, Fu DH, et al. Prevalence study of age-related macular degeneration over the age of 50's in Wuxi[J]. Chin J Pract Ophthalmol, 2013, 31(4): 494-498. DOI: 10.3760/cma.j.issn.1006-4443.2013.04.032.
44. 杨倩, 韩毳, 刘宁, 等. 免散瞳数码眼底照相在眼底黄斑部疾病筛查中的应用[J]. 眼科新进展, 2012, 32(5): 473-475. DOI: 10.13389/j.cnki.rao.2012.05.010.Yang Q, Han C, Liu N, et al. Application of non-mydriatic digital fundus photography in fundus macular disease screening[J]. Rec Adv Ophthalmol, 2012, 32(5): 473-475. DOI: 10.13389/j.cnki.rao.2012.05.010.
45. Jonas JB, Xu L, Wang YX. The Beijing eye study[J]. Acta ophthalmologica, 2009, 87(3): 247-261. DOI: 10.1111/j.1755-3768.2008.01385.x.
46. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning[J]. Big Data, 2019, 6(1): 1-48. DOI: 10.1186/s40537-019-0197-0.
47. Padilla R, Netto SL, Da Silva EA. A survey on performance metrics for object-detection algorithms[C]. 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Wuhan, 2020: 237-242.
48. Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
49. 中华医学会健康管理学分会, 《中华健康管理学杂志》编辑委员会. 健康体检主检报告撰写专家共识[J]. 中华健康管理学杂志, 2020, 14(1): 8-11. DOI: 10.3760/cma.j.issn.1674-0815.2020.01.003.Chinese Society of Health Management, The Editorial Board of Chinese Journal of Heatlth Management. Expert consensus on the chief physician report for health checkup[J]. Chin J Health Manage, 2020, 14(1): 8-11. DOI: 10.3760/cma.j.issn.1674-0815.2020.01.003.
50. 国家食品药品监督管理总局. 中华人民共和国医药行业标准: 眼科仪器眼底照相机[S]. 北京: 中国标准出版社, 2008.National Medical Products Administration. People's Republic of China Pharmaceutical Industry Standard: ophthalmic instruments fundus camera[S]. Beijing: China Standards Press, 2008.
51. 宋琳琳, 李志清, 马文江, 等. 运用云医疗远程平台构建糖尿病视网膜病变分级诊疗模式[J]. 现代医院管理, 2020, 18(3): 17-20. DOI: 10.3969/j.issn.1672-4232.2020.03.005.Song LL, Li ZQ, Ma WJ, et al. Establishment of a hierarchical diagnosis and treatment model for diabetic retinopathy by cloud medical remote platform[J]. Modern Hospital Management, 2020, 18(3): 17-20. DOI: 10.3969/j.issn.1672-4232.2020.03.005.
52. 谭丹华, 万里飞, 邹溢辉. 医院信息系统HIS和及其应用[J]. 中国医疗器械信息, 2007, 13(9): 39-40. DOI: 10.3969/j.issn.1006-6586.2007.09.011.Tan DH, Wan LF, Zou YH. Hospital information system and its application[J]. China Medical Device Information, 2007, 13(9): 39-40. DOI: 10.3969/j.issn.1006-6586.2007.09.011.
53. 李贵祥, 刘国祥, 李刚荣, 等. 大型综合性医院 PACS 系统设计与实施[J]. 中国医院管理, 2005, 25(1): 46-48. DOI: 10.3969/j.issn.1001-5329.2005.01.019.Li GX, Liu GX, Li GR, et al. The design and implementation of PACS system in large-scale comprehensive hospitals[J]. Chinese Hospital Management, 2005, 25(1): 46-48. DOI: 10.3969/j.issn.1001-5329.2005.01.019.
54. Long M, Zhu H, Wang J, et al. Unsupervised domain adaptation with residual transfer networks[C]. 30th International Conference on Neural Information Processing Systems, New York: Curran Associates Inc. , 2016: 136-144.
55. Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]. The 32 nd International Conference on Machine Learning, Lille, 2015: 1180-1189.

Chinese Journal of Ocular Fundus Diseases

The standardized design and application guidelines:a primary-oriented artificial intelligence screening system of the lesion sign in the macular region based on fundus color photography

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