Journal of Shandong University (Health Sciences) ›› 2020, Vol. 58 ›› Issue (11): 11-16.doi: 10.6040/j.issn.1671-7554.0.2020.1173

• Special topic on new progress in ophthalmic artificial intelligence • Previous Articles     Next Articles

Research progress of artificial intelligence in childhood eye diseases

Haotian LIN*(),Longhui LI,Jingjing CHEN   

  1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, Guangdong, China
  • Received:2020-08-17 Online:2020-11-10 Published:2020-11-04
  • Contact: Haotian LIN E-mail:linht5@mail.sysu.edu.cn

RichHTML

33

PDF (PC)

338

Abstract:

Childhood is a critical period of visual system development, during which eye diseases can easily lead to irreversible visual impairment and cause heavy family and socioeconomic burdens. Early screening and early treatment have always been essential, but due to the shortage of pediatric ophthalmologists, it is very difficult to carry out large-scale screening. With the tremendous progress of data processing technology, artificial intelligence is used greatly in the medical field. At present, artificial intelligence has been widely studied and applied in the fields of retinopathy of prematurity (ROP), congenital cataract, strabismus, refractive error and visual function screening. It has excellent performance in early screening, diagnosis and staging, treatment recommendations, and prognosis prediction of a variety of childhood eye diseases. However, childhood eye diseases are far less focused than adult eye diseases, and there are still many problems to be solved.

Key words: Artificial intelligence, Machine learning, Deep learning, Childhood eye diseases

CLC Number: 

  • R779.7
1 Lee A , Taylor P , Kalpathy-Cramer J , et al. Machine learning has arrived![J]. Ophthalmology, 2017, 124 (12): 1726- 1728.
doi: 10.1016/j.ophtha.2017.08.046
2 Rahimy E . Deep learning applications in ophthalmology[J]. Curr Opin Ophthalmol, 2018, 29 (3): 254- 260.
doi: 10.1097/ICU.0000000000000470
3 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
4 Zimmermann A , Carvalho KMMd , Atihe C , et al. Visual development in children aged 0 to 6 years[J]. Arq Bras Oftalmol, 2019, 82 (3): 173- 175.
5 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
6 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.
doi: 10.1167/iovs.18-23887
7 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
8 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
9 Jonas JB , Aung T , Bourne RR , et al. Glaucoma[J]. Lancet, 2017, 390 (10108): 2183- 2193.
doi: 10.1016/S0140-6736(17)31469-1
10 De Clerck EEB , Schouten JSAG , Berendschot TTJM , et al. New ophthalmologic imaging techniques for detection and monitoring of neurodegenerative changes in diabetes: a systematic review[J]. Lancet Diabetes Endocrinol, 2015, 3 (8): 653- 663.
doi: 10.1016/S2213-8587(15)00136-9
11 Wong WL , Su X , Li X , et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis[J]. Lancet Glob Health, 2014, 2 (2): 106- 116.
doi: 10.1016/S2214-109X(13)70145-1
12 Lin H , Zhang L , Lin D , et al. Visual restoration after cataract surgery promotes functional and structural brain recovery[J]. EBioMedicine, 2018, 30: 52- 61.
doi: 10.1016/j.ebiom.2018.03.002
13 Rajkomar A , Dean J , Kohane I . Machine learning in medicine[J]. N Engl J Med, 2019, 380 (14): 1347- 1358.
doi: 10.1056/NEJMra1814259
14 Esteva A , Robicquet A , Ramsundar B , et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25 (1): 24- 29.
doi: 10.1038/s41591-018-0316-z
15 Deo RC . Machine Learning in medicine[J]. Circulation, 2015, 132 (20): 1920- 1930.
doi: 10.1161/CIRCULATIONAHA.115.001593
16 Asaoka R , Murata H , Hirasawa K , et al. Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images[J]. Am J Ophthalmol, 2019, 198: 136- 145.
doi: 10.1016/j.ajo.2018.10.007
17 Wu X , Huang Y , Liu Z , et al. Universal artificial intelligence platform for collaborative management of cataracts[J]. Br J Ophthalmol, 2019, 103 (11): 1553- 1560.
doi: 10.1136/bjophthalmol-2019-314729
18 黄玉梅, 麦菁芸, 杨祖钦, 等. 广角数码视网膜成像系统与间接检眼镜在早产儿眼底病变筛查中的应用比较[J]. 中华眼底病杂志, 2017, 33 (1): 64- 66.
19 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
20 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
21 Wallace DK , Zhao Z , Freedman SF . A pilot study using "ROPtool" to quantify plus disease in retinopathy of prematurity[J]. J AAPOS, 2007, 11 (4): 381- 387.
doi: 10.1016/j.jaapos.2007.04.008
22 Gelman R , Martinez-Perez ME , Vanderveen DK , et al. Diagnosis of plus disease in retinopathy of prematurity using Retinal Image multiScale Analysis[J]. Invest Ophthalmol Vis Sci, 2005, 46 (12): 4734- 4738.
doi: 10.1167/iovs.05-0646
23 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: 803- 810.
doi: 10.1001/jamaophthalmol.2018.1934
24 Wang J , Ju R , Chen Y , 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
25 Lin H , Long E , Chen W , et al. Documenting rare disease data in China[J]. Science, 2015, 349 (6252): 1064.
26 Long E, Lin H, Liu Z, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts[J]. Nat Biomed Eng, 1, 0024 (2017). doi: 10.1038/s41551-016-0024.
27 Lin H , Li R , Liu Z , et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial[J]. EClinicalMedicine, 2019, 9: 52- 59.
doi: 10.1016/j.eclinm.2019.03.001
28 Lin D , Chen J , Lin Z , et al. A practical model for the identification of congenital cataracts using machine learning[J]. EBioMedicine, 2020, 51: 102621.
doi: 10.1016/j.ebiom.2019.102621
29 Lin D , Liu Z , Chen J , et al. Practical pattern of surgical timing of childhood cataract in China: a cross-sectional database study[J]. Int J Surg, 2019, 62: 56- 61.
doi: 10.1016/j.ijsu.2019.01.012
30 Zhang K , Liu X , Jiang J , et al. Prediction of postoperative complications of pediatric cataract patients using data mining[J]. J Transl Med, 2019, 17 (1): 2.
doi: 10.1186/s12967-018-1758-2
31 Gunton KB , Wasserman BN , DeBenedictis C . Strabismus[J]. Primary Care Clinics in Office Practice, 2015, 42 (3): 393- 407.
doi: 10.1016/j.pop.2015.05.006
32 Lu J, Fan Z, Zheng C, et al. Automated strabismus detection for telemedicine applications[J]. arXiv, 2018, 1809.02940.
33 Chen Z , Fu H , Lo WL , et al. Strabismus recognition using eye-tracking data and convolutional neural networks[J]. J Healthc Eng, 2018, 2018: 7692198.
doi: 10.1155/2018/7692198
34 Gramatikov BI . Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning[J]. Biomed Eng Online, 2017, 16 (1): 52.
doi: 10.1186/s12938-017-0339-6
35 Ikuno Y . Overview of the complications of high myopia[J]. Retina, 2017, 37 (12): 2347- 2351.
doi: 10.1097/IAE.0000000000001489
36 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 Medicine, 2018, 15 (11): e1002674.
doi: 10.1371/journal.pmed.1002674
37 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.
doi: 10.21037/atm.2019.12.39
38 Van Eenwyk J , Agah A , Giangiacomo J , et al. Artificial intelligence techniques for automatic screening of amblyogenic factors[J]. Trans Am Ophthalmol Soc, 2008, 106: 64- 73.
39 Long E , Liu Z , Xiang Y , et al. Discrimination of the behavioural dynamics of visually impaired infants via deep learning[J]. Nature Biomedical Engineering, 2019, 3 (11): 860- 869.
doi: 10.1038/s41551-019-0461-9
40 Nilsson Benfatto M , qvist Seimyr G , Ygge J , et al. Screening for dyslexia using eye tracking during reading[J]. PLoS One, 2016, 11 (12): e0165508.
doi: 10.1371/journal.pone.0165508
41 Vogelsang L , Gilad-Gutnick S , Ehrenberg E , et al. Potential downside of high initial visual acuity[J]. Proc Natl Acad Sci U S A, 2018, 115 (44): 11333- 11338.
doi: 10.1073/pnas.1800901115
42 Owen CG , Rudnicka AR , Mullen R , et al. Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program[J]. Invest Ophthalmol Vis Sci, 2009, 50 (5): 2004- 2010.
doi: 10.1167/iovs.08-3018
43 Beers A, Brown J, Chang K, et al. High-resolution medical image synthesis using progressively grown generative adversarial networks[J]. arXiv, 2018, 1805.03144.
44 林铎儒, 吴晓航, 刘臻臻. 眼科开展医学人工智能研究的学科优势[J]. 中国临床新医学, 2020, 13 (2): 127- 129.
LIN Duoru , WU Xiaohan , LIU Zhenzhen . Discipline advantage of medical artificial intelligence in ophthalmology research[J]. Chinese Journal of New Clinical Medicine, 2020, 13 (2): 127- 129.
[1] Ju LIU,Qiang WU,Luyue YU,Fengming LIN. Brain tumor image segmentation based on deep learning techniques [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 42-49, 73.
[2] Xingang LI,Xin ZHANG,Anjing CHEN. The latest advances in human brain projects [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 5-9, 21.
[3] Jizong ZHAO. Neurosurgery plays a key role in brain science research [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 1-4.
[4] Qiang WU,Zekun HE,Ju LIU,Xiaomeng CUI,Shuang SUN,Wei SHI. A research on multi-modal MRI analysis based on machine learning for brain glioma [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 81-87.
[5] Wei ZHANG,Wenhao TAN,Yibin LI. Locmotion control of quadruped robot based on deep reinforcement learning: review and prospect [J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 61-66.
[6] QU Yi, ZHANG Huankai, SONG Xian, CHU Baorui. Research progress of artificial intelligence diagnosis system in retinal diseases [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 39-44.
[7] Carol Y. Cheung, RAN Anran. Artificial intelligence deep learning in glaucoma imaging: current progress and future prospect [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 24-32.
[8] GE Zongyuan, HE Wanji, JU Lie, YAO Xuan, WANG Lin, HUANG Yelin, YANG Zhiwen, XIONG Jianhao, BAO Yining, LI Ming, ZHANG Bing, ZHAO Xin. New developments in ophthalmic AI algorithms [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 17-23.
[9] Mingguang HE,Chi LIU,Zhixi LI. Applying artificial intelligence in ophthalmic real-world practice: opportunities and challenges [J]. Journal of Shandong University (Health Sciences), 2020, 58(11): 1-10.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] SUO Dongyang, SHEN Fei, GUO Hao, LIU Lichang, YANG Huimin, YANG Xiangdong. Expression and mechanism of Tim-3 in animal model of drug-induced acute kidney injury[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 1 -6 .
[2] MA Qingyuan, PU Peidong, HAN Fei, WANG Chao, ZHU Zhoujun, WANG Weishan, SHI Chenhui. Effect of miR-27b-3p regulating SMAD1 on osteosarcoma cell proliferation, migration and invasion[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 32 -37 .
[3] LI Ning, LI Juan, XIE Yan, LI Peilong, WANG Yunshan, DU Lutao, WANG Chuanxin. Expression of LncRNA AL109955.1 in 80 cases of colorectal cancer and its effect on cell proliferation, migration and invasion[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 38 -46 .
[4] XU Yuxiang, LIU Yudong, ZHANG Peng, DUAN Ruisheng. A retrospective analysis of risk factors of cerebral microbleeds in 101 patients with cerebral small vessel disease[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 67 -71 .
[5] LUO Xin, HE Bing, NIE Qingsheng, HOU Zhenbo, DONG Jun, LI Yuhua, ZENG Xiangqin, LIU Wei, KONG Demin, CAO Jinfeng. Comparison of the value of mono-exponential mode and diffusion kurtosis imaging mode in grading clear cell renal cell carcinoma using magnetic resonance diffusion-weighted imaging[J]. Journal of Shandong University (Health Sciences), 2020, 1(7): 89 -95 .
[6] Di ZHANG,Meng YU,Xia LIU. A general overview of neuromodulation[J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 50 -60 .
[7] Wei ZHANG,Wenhao TAN,Yibin LI. Locmotion control of quadruped robot based on deep reinforcement learning: review and prospect[J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 61 -66 .
[8] Anjing CHEN,Xun ZHANG. The new strategies of targeting SUMOylation in the treatment of glioma[J]. Journal of Shandong University (Health Sciences), 2020, 1(8): 88 -94 .
[9] ZHANG Hongbin, ZHAO Hanhui, WANG Suxia, ZHOU Peng, HE Qingqing, WANG Yanqun, DING Weiping, LIU Gang. Perioperative observation and postoperative risk factors of severe hypocalcemia after parathyroidectomy: a report of 303 cases[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 14 -20 .
[10] SUN Yifeng, GAO Yu, LIANG Yongyuan, GAO Yang. Expression of CPLX2 and its in vitro effects on the proliferation migration and invasion of hepatocellular carcinoma cells[J]. Journal of Shandong University (Health Sciences), 2020, 1(9): 34 -39 .
Copyright 2018 © Editorial office of Journal of Shandong University (Health Sciences)
Supported by Beijing Magtech Co.Ltd