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
Haotian LIN*(
),Longhui LI,Jingjing CHEN
CLC Number:
| 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] | LIANG Chen, YAO Lijuan, LYU Longfei, TANG Ze, QIN Da, CUI Youbin,YU Xiaoqi. Application of 3D reconstruction combined with CT-guided puncture localization in minimally invasive treatment of pulmonary ground-glass nodules [J]. Journal of Shandong University (Health Sciences), 2026, 64(5): 74-82. |
| [2] | LIAO Yuan, MEN Dan, LI Yifan, LI Huaichen, LONG Fei, LIU Yi. Short-term effect of PM2.5 on the incidence of tuberculosis based on individual precise exposure assessment [J]. Journal of Shandong University (Health Sciences), 2026, 64(3): 116-123. |
| [3] | Intelligent Orthopedics Subgroup of Chinese Association of Orthopedic, Subgroup for Prevention and Control of Spinal and Spinal Cord Injury Diseases of Professional Committee for Prevention and Control of Spinal Diseases of Chinese Preventive Medicine Association. Expert consensus on measurement sites and annotation of artificial intelligence-based spinal degenerative imaging(2025) [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 1-10. |
| [4] | LIU Yu, HUO Yaya, GONG Cheng, LIANG Ting, LI Bin. Design and optimization of orthopedic biomaterials based on machine learning [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 22-33. |
| [5] | JI Xinyu, YU Siyi, SUN Yuanyuan, JI Bing. Orthopedic disease diagnosis and treatment assistance methods based on artificial intelligence and gait analysis [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 34-43. |
| [6] | WANG Jianmin, LI Xiaofeng, YOU Zhitao, DONG Shengjie, ZHAO Yuchi, LI Zhanju, ZOU Dexin, ZHANG Jianfeng, SUN Tao, DU Wei. Construction of a chronic post-surgical pain prediction model for posterior lumbar interbody fusion surgery based on interpretable machine learning [J]. Journal of Shandong University (Health Sciences), 2026, 64(2): 78-88. |
| [7] | YANG Fan. Multimodal medical data fusion technology and its application [J]. Journal of Shandong University (Health Sciences), 2025, 63(8): 17-40. |
| [8] | WANG Liyun, GAO Tianqin, LIU Yujia, CHEN Qing, CHEN Liu, SHA Kaihui. Development and validation of a postpartum stress urinary incontinence risk prediction model based on machine learning [J]. Journal of Shandong University (Health Sciences), 2025, 63(6): 55-66. |
| [9] | ZHANG Xinru, LI Yang, SUN Meng, NIE Wei, MA Zhe. Application and evaluation of Vision-LSTM model in diagnostic ultrasound imaging of Thyroid Imaging Reporting and Data System Category 4b thyroid nodules [J]. Journal of Shandong University (Health Sciences), 2025, 63(11): 68-74. |
| [10] | WU Qiqi, CHENG Miaomiao, XIAO Xiaoyan. Multimodal models in the field of kidney disease [J]. Journal of Shandong University (Health Sciences), 2025, 63(10): 117-124. |
| [11] | LIANG Bowen, LU Qingsheng. Advances in robotic-assisted endovascular aortic repair [J]. Journal of Shandong University (Health Sciences), 2024, 62(9): 61-65. |
| [12] | TANG Yuning, PAN Tianyue, DONG Zhihui, FU Weiguo. Research progress of deep learning in automatic segmentation of aortic images [J]. Journal of Shandong University (Health Sciences), 2024, 62(9): 66-73. |
| [13] | WANG Jing, LIU Xiaofei, ZENG Rong, XU Changjuan, ZHANG Jintao, DONG Liang. Identification of necroptosis-related biomarkers in asthma based on machine learning algorithms [J]. Journal of Shandong University (Health Sciences), 2024, 62(7): 21-32. |
| [14] | GUO Zhenjiang, WANG Ning, ZHAO Guangyuan, DU Liqiang, CUI Zhaobo, LIU Fangzhen. Development of preoperative models for predicting positive esophageal margin in proximal gastric cancer based on machine learning [J]. Journal of Shandong University (Health Sciences), 2024, 62(7): 78-83. |
| [15] | LIANG Yongyuan, CAI Peifei, ZHENG Guixi. Establishment and value assessment of colon cancer diagnostic models based on multiple variables and different machine learning algorithms [J]. Journal of Shandong University (Health Sciences), 2024, 62(2): 51-59. |
|
||