山东大学耳鼻喉眼学报 ›› 2023, Vol. 37 ›› Issue (3): 157-162.doi: 10.6040/j.issn.1673-3770.0.2022.374
• 综述 • 上一篇
杜曰山一1,2,王鲜3,张国明1,2
DU Yueshanyi1,2, WANG Xian3, ZHANG Guoming1,2
摘要: 早产儿视网膜病变(retinopathy of prematurity, ROP)是导致儿童致盲的主要原因之一,早期ROP筛查和诊断高度依赖眼科专科医生,而随着现代医学影像技术的飞速发展和远程医疗的兴起,人工智能(artificial intelligence, AI)在ROP领域也得到了进一步应用。近年来,基于卷积神经网络、深度学习的AI在眼科筛查检测、诊疗领域得到更广泛深入地应用,其在ROP的临床诊疗方面尤引人注目,有望提高基层儿科和眼科医生对ROP早期诊断、规范治疗等方面的能力,同时降低主管医生之间的主观差异。论文概述AI在ROP自动筛查检测、诊断及预测的研究现状、存在的不足及面临的挑战,进一步了解其在ROP临床应用新动态和新进展。
中图分类号:
[1] Solebo AL, Teoh L, Rahi J. Epidemiology of blindness in children[J]. Arch Dis Child, 2017, 102(9): 853-857. doi:10.1136/archdischild-2016-310532 [2] Li JP O, Liu HR, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: a global perspective[J]. Prog Retin Eye Res, 2021, 82: 100900. doi:10.1016/j.preteyeres.2020.100900 [3] Wu T, Zhang L, Tong Y, et al. Retinopathy of prematurity among very low-birth-weight infants in China: incidence and perinatal risk factors[J]. Invest Ophthalmol Vis Sci, 2018, 59(2): 757-763. doi:10.1167/iovs.17-23158 [4] 黎晓新. 我国早产儿视网膜病变特点和筛查指南[J]. 中华眼底病杂志, 2004(6): 384-386. LI Xiaoxin. Characteristics and screening guidelines of retinopathy of prematurity in China[J]. Chinese Journal of Ocular Fundus Diseases, 2004(6): 384-386. [5] Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology[J]. Int J Ophthalmol, 2018, 11(9): 1555-1561. doi:10.18240/ijo.2018.09.21 [6] 刘潇逸, 项毅帆, 杨扬帆, 等. 婴幼儿眼病的人工智能应用[J]. 眼科学报, 2022, 37(3): 214-221. LIU Xiaoyi, XIANG Yifan, YANG Yangfan, et al. Artificial intelligence application for infantile eye diseases[J]. Eye Science, 2022, 37(3): 214-221. [7] 华红利, 李松, 陶泽璋. 人工智能在鼻咽癌诊疗中的研究进展[J]. 山东大学耳鼻喉眼学报, 2022, 36(2): 113-119. doi: 10.6040/j.issn.1673-3770.0.2021.175 HUA Hongli, LI Song, TAO Zezhang. Research progress of artificial intelligence in the diagnosis and treatment of nasopharyngeal carcinoma[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2022, 36(2): 113-119. doi: 10.6040/j.issn.1673-3770.0.2021.175 [8] 朱志玲, 李松, 管国芳. 人工智能在耳鼻咽喉头颈外科的运用及展望[J]. 山东大学耳鼻喉眼学报, 2020, 34(2): 115-120. doi: 10.6040/j.issn.1673-3770.0.2019.598 ZHU Zhiling, LI Song, GUAN Guofang. Application and prospect of artificial intelligence in otolaryngology[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2020, 34(2): 115-120. doi: 10.6040/j.issn.1673-3770.0.2019.598 [9] 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 [10] 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. doi:10.1038/s41591-018-0107-6 [11] Ting DSW, Cheung CYL, 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. doi:10.1001/jama.2017.18152 [12] 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), 2020, 58(11): 11-16. doi:10.6040/j.issn.1671-7554.0.2020.1173 LIN Haotian, LI Longhui, CHEN Jingjing. Research progress of artificial intelligence in childhood eye diseases[J]. Journal of Shandong University(Health Science), 2020, 58(11): 11-16. doi:10.6040/j.issn.1671-7554.0.2020.1173 [13] Wallace DK, Zhao ZE, Freedman SF. A pilot study using “ROPtool” to quantify plus disease in retinopathy of prematurity[J]. J Am Assoc Pediatr Ophthalmol Strabismus, 2007, 11(4): 381-387. doi:10.1016/j.jaapos.2007.04.008 [14] Heneghan C, Flynn J, O'Keefe M, et al. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis[J]. Med Image Anal, 2002, 6(4): 407-429. doi:10.1016/S1361-8415(02)00058-0 [15] Rabinowitz MP, Grunwald JE, Karp KA, et al. Progression to severe retinopathy predicted by retinal vessel diameter between 31 and 34 weeks of postconception age[J]. Arch Ophthalmol, 2007, 125(11): 1495-1500. doi:10.1001/archopht.125.11.1495 [16] Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks[J]. JAMA Ophthalmol, 2018, 136(7): 803-810. doi:10.1001/jamaophthalmol.2018.1934 [17] Choi RY, Brown JM, Kalpathy-Cramer J, et al. Variability in plus disease identified using a deep learning-based retinopathy of prematurity severity scale[J]. Ophthalmol Retina, 2020, 4(10): 1016-1021. doi:10.1016/j.oret.2020.04.022 [18] Campbell JP, Kim SJ, Brown JM, et al. Evaluation of a deep learning-derived quantitative retinopathy of prematurity severity scale[J]. Ophthalmology, 2021, 128(7): 1070-1076. doi:10.1016/j.ophtha.2020.10.025 [19] Taylor S, Brown JM, Gupta K, et al. Monitoring disease progression with a quantitative severity scale for retinopathy of prematurity using deep learning[J]. JAMA Ophthalmol, 2019, 137(9): 1022-1028. doi:10.1001/jamaophthalmol.2019.2433 [20] Gupta K, Campbell JP, Taylor S, et al. A quantitative severity scale for retinopathy of prematurity using deep learning to monitor disease regression after treatment[J]. JAMA Ophthalmol, 2019, 137(9): 1029-1036. doi:10.1001/jamaophthalmol.2019.2442 [21] Bellsmith KN, Brown J, Kim SJ, et al. Aggressive posterior retinopathy of prematurity: clinical and quantitative imaging features in a large North American cohort[J]. Ophthalmology, 2020, 127(8): 1105-1112. doi:10.1016/j.ophtha.2020.01.052 [22] Campbell JP, Chiang MF, Chen JS, et al. Artificial intelligence for retinopathy of prematurity: validation of a vascular severity scale against international expert diagnosis[J]. Ophthalmology, 2022, 129(7): e69-e76. doi:10.1016/j.ophtha.2022.02.008 [23] Chiang MF, Quinn GE, Fielder AR, et al. International classification of retinopathy of prematurity, third edition[J]. Ophthalmology, 2021, 128(10): e51-e68. doi:10.1016/j.ophtha.2021.05.031 [24] International Committee for the Classification of Retinopathy of Prematurity. The international classification of retinopathy of prematurity revisited[J]. Arch Ophthalmol, 2005, 123(7): 991-999. doi:10.1001/archopht.123.7.991 [25] 郭宝, 张德勇. 康柏西普联合激光治疗急进性早产儿视网膜病变[J]. 山东大学耳鼻喉眼学报, 2018, 32(6): 92-97. doi:10.6040/j.issn.1673-3770.0.2018.272 GUO Bao, ZHANG Deyong. Clinical study of compaq combined with laser in the treatment of retinopathy of prematurity[J]. Journal of Otolaryngology and Ophthalmology of Shandong University, 2018, 32(6): 92-97. doi:10.6040/j.issn.1673-3770.0.2018.272 [26] Zhao JF, Lei BY, Wu ZQ, et al. A deep learning framework for identifying zone I in RetCam images[J]. IEEE Access, 2019, 7: 103530-103537. doi:10.1109/ACCESS.2019.2930120 [27] Zhang RG, Zhao JF, Chen GZ, et al. Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network[C] //International Workshop on Ophthalmic Medical Image Analysis. Cham: Springer, 2019: 165-172.10.1007/978-3-030-32956-3_20 [28] Agrawal R, Kulkarni S, Walambe R, et al. Assistive framework for automatic detection of all the zones in retinopathy of prematurity using deep learning[J]. J Digit Imaging, 2021, 34(4): 932-947. doi:10.1007/s10278-021-00477-8 [29] Peng YY, Chen ZY, Zhu WF, et al. Automatic zoning for retinopathy of prematurity with semi-supervised feature calibration adversarial learning[J]. Biomed Opt Express, 2022, 13(4): 1968-1984. doi:10.1364/BOE.447224 [30] Ng WY, Zhang SH, Wang ZR, et al. Updates in deep learning research in ophthalmology[J]. Clin Sci(Lond), 2021, 135(20): 2357-2376. doi:10.1042/CS20210207 [31] Peng YY, Zhu WF, Chen ZY, et al. Automatic staging for retinopathy of prematurity with deep feature fusion and ordinal classification strategy[J]. IEEE Trans Med Imaging, 2021, 40(7): 1750-1762. doi:10.1109/TMI.2021.3065753 [32] Attallah O. DIAROP: automated deep learning-based diagnostic tool for retinopathy of prematurity[J]. Diagnostics(Basel), 2021, 11(11): 2034. doi:10.3390/diagnostics11112034 [33] Hu JJ, Chen YY, Zhong J, et al. Automated analysis for retinopathy of prematurity by deep neural networks[J]. IEEE Trans Med Imaging, 2019, 38(1): 269-279. doi:10.1109/TMI.2018.2863562 [34] Wang JY, Ju R, Chen YY, et al. Automated retinopathy of prematurity screening using deep neural networks[J]. EBioMedicine, 2018, 35: 361-368. doi:10.1016/j.ebiom.2018.08.033 [35] Zhang YS, Wang L, Wu ZQ, et al. Development of an automated screening system for retinopathy of prematurity using a deep neural network for wide-angle retinal images[J]. IEEE Access, 2018, 7: 10232-10241. doi:10.1109/ACCESS.2018.2881042 [36] Wu QW, Hu YJ, Mo ZY, et al. Development and validation of a deep learning model to predict the occurrence and severity of retinopathy of prematurity[J]. JAMA Netw Open, 2022, 5(6): e2217447. doi:10.1001/jamanetworkopen.2022.17447 [37] Iu LPL, Yip WWK, Lok JYC, et al. Prediction model to predict type 1 retinopathy of prematurity using gestational age and birth weight(PW-ROP)[J]. Br J Ophthalmol, 2022: bjophthalmol-2021-320670. doi:10.1136/bjophthalmol-2021-320670 [38] Coyner AS, Chen JS, Singh P, et al. Single-examination risk prediction of severe retinopathy of prematurity[J]. Pediatrics, 2021, 148(6): e2021051772. doi:10.1542/peds.2021-051772 [39] Schmidt-Erfurth U, Sadeghipour A, Gerendas BS, et al. Artificial intelligence in retina[J]. Prog Retin Eye Res, 2018, 67: 1-29. doi:10.1016/j.preteyeres.2018.07.004 |
[1] | 韩飞燕,王英. 鼻腔鼻窦神经鞘瘤13例临床分析[J]. 山东大学耳鼻喉眼学报, 2023, 37(2): 21-25. |
[2] | 刘佳钰,樊慧明,邹游,陈始明. 人工智能在鼻咽癌诊断与治疗中的应用研究进展[J]. 山东大学耳鼻喉眼学报, 2023, 37(2): 135-142. |
[3] | 肖富亮,林云,潘新良. 早期cN0 PTC预防性中央区淋巴结清扫的临床研究[J]. 山东大学耳鼻喉眼学报, 2023, 37(1): 64-71. |
[4] | 程雷,许秋艳,陈浩. 变态反应检测与诊断的临床应用及意义[J]. 山东大学耳鼻喉眼学报, 2022, 36(3): 1-6. |
[5] | 熊攀辉,沈暘,杨玉成. 基于表型和内在型的慢性鼻窦炎诊治进展[J]. 山东大学耳鼻喉眼学报, 2022, 36(3): 15-19. |
[6] | 华红利,李松,陶泽璋. 人工智能在鼻咽癌诊疗中的研究进展[J]. 山东大学耳鼻喉眼学报, 2022, 36(2): 113-119. |
[7] | 芦晓妍, 温树信. 先天性后鼻孔闭锁的治疗进展[J]. 山东大学耳鼻喉眼学报, 2022, 36(1): 138-142. |
[8] | 黄天泽,陈迪,李莹. 机器学习在眼表疾病诊断及角膜手术中的应用进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 13-19. |
[9] | 万怡宁,张德军,傅则名,郭芳,郭颖媛,管国芳. 磁共振弥散加权成像在先天性中耳胆脂瘤精准诊断与JOS分期中的应用探讨12例[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 65-69. |
[10] | 王迪,程金章,于丹. 基于机器学习的人工智能技术在耳鼻喉科临床诊疗中的应用进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(6): 125-131. |
[11] | 刘寨,应民政. 环状RNA在变应性鼻炎中的研究进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(5): 105-112. |
[12] | 冉宏运,蒋可可,张杰. 早产儿视网膜病变患儿屈光影响因素研究进展[J]. 山东大学耳鼻喉眼学报, 2021, 35(5): 118-124. |
[13] | 吴迪盼盼,崔新华,郭颖,耿博,高芳芳,梁辉. 窄带成像技术在咽喉反流诊断中的优势应用[J]. 山东大学耳鼻喉眼学报, 2021, 35(3): 31-36. |
[14] | 季颜平,薛宇,林岚. 头颈部结节性筋膜炎临床病理分析[J]. 山东大学耳鼻喉眼学报, 2021, 35(2): 76-79. |
[15] | 袁康龙,肖旭平. 坏死性颈筋膜炎的临床诊治进展[J]. 山东大学耳鼻喉眼学报, 2020, 34(6): 135-138. |
|