Citation: | ZHANG Xiao, CHEN Youxin. Current Situation and Prospect of Teleophthalmology[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 755-760. DOI: 10.12290/xhyxzz.2021-0198 |
[1] |
Dorsey ER, Topol EJ. State of Telehealth[J]. N Engl J Med, 2016, 375: 154-161. DOI: 10.1056/NEJMra1601705
|
[2] |
Parikh D, Armstrong G, Liou V, et al. Advances in Telemedicine in Ophthalmology[J]. Semin Ophthalmol, 2020, 35: 210-215. DOI: 10.1080/08820538.2020.1789675
|
[3] |
Hadziahmetovic M, Nicholas P, Jindal S, et al. Evaluation of a Remote Diagnosis Imaging Model vs Dilated Eye Examination in Referable Macular Degeneration[J]. JAMA Ophthalmol, 2019, 137: 802-808. DOI: 10.1001/jamaophthalmol.2019.1203
|
[4] |
Grau E, Horn F, Nixdorff U, et al. OCT and IOP findings in a healthy worker cohort: results from a teleophthalmic study in occupational medicine[J]. Graefes Arch Clin Exp Ophthalmol, 2019, 257: 2571-2578. DOI: 10.1007/s00417-019-04457-1
|
[5] |
Saeedi P, Petersohn I, Salpea P, et al. IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 th edition[J]. Diabetes Res Clin Pract, 2019, 157: 107843. DOI: 10.1016/j.diabres.2019.107843
|
[6] |
Yau JW, Rogers SL, Kawasaki R, et al. Meta-Analysis for Eye Disease (META-EYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy[J]. Diabetes Care, 2012, 35: 556-564. DOI: 10.2337/dc11-1909
|
[7] |
Wong TY, Sabanayagam C. Strategies to Tackle the Global Burden of Diabetic Retinopathy: From Epidemiology to Artificial Intelligence[J]. Ophthalmologica, 2020, 243: 9-20. DOI: 10.1159/000502387
|
[8] |
Scotland GS, McNamee P, Fleming AD, et al. Scottish Diabetic Retinopathy Clinical Research Network. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy[J]. Br J Ophthalmol, 2010, 94: 712-719. DOI: 10.1136/bjo.2008.151126
|
[9] |
Nguyen HV, Tan GS, Tapp RJ, et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore[J]. Ophthalmology, 2016, 123: 2571-2580. DOI: 10.1016/j.ophtha.2016.08.021
|
[10] |
Horton MB, Brady CJ, Cavallerano J, et al. Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition[J]. Telemed J E Health, 2020, 26: 495-543. DOI: 10.1089/tmj.2020.0006
|
[11] |
Fonda SJ, Bursell SE, Lewis DG, et al. The Indian Health Service Primary Care-Based Teleophthalmology Program for Diabetic Eye Disease Surveillance and Management[J]. Telemed J E Health, 2020, 26: 1466-1474. DOI: 10.1089/tmj.2019.0281
|
[12] |
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: 2211-2223. DOI: 10.1001/jama.2017.18152
|
[13] |
Trese MT. What is the real gold standard for ROP screening?[J]. Retina, 2008, 28: S1-S2. DOI: 10.1097/IAE.0b013e31816a5587
|
[14] |
Brady CJ, D'Amico S, Campbell JP. Telemedicine for Retinopathy of Prematurity[J]. Telemed J E Health, 2020, 26: 556-564. DOI: 10.1089/tmj.2020.0010
|
[15] |
Shah PK, Ramya A, Narendran V. Telemedicine for ROP[J]. Asia Pac J Ophthalmol (Phila), 2018, 7: 52-55.
|
[16] |
Li JO, Liu H, 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
|
[17] |
Court JH, Austin MW. Virtual glaucoma clinics: patient acceptance and quality of patient education compared to standard clinics[J]. Clin Ophthalmol, 2015, 9: 745-749. http://www.dovepress.com/virtual-glaucoma-clinics-patient-acceptance-and-quality-of-patient-edu-peer-reviewed-article-OPTH
|
[18] |
Odden JL, Khanna CL, Choo CM, et al. Telemedicine in long-term care of glaucoma patients[J]. J Telemed Telecare, 2020, 26: 92-99. DOI: 10.1177/1357633X18797175
|
[19] |
Gan K, Liu Y, Stagg B, et al. Telemedicine for Glaucoma: Guidelines and Recommendations[J]. Telemed J E Health, 2020, 26: 551-555. DOI: 10.1089/tmj.2020.0009
|
[20] |
Brady CJ, Garg S. Telemedicine for Age-Related Macular Degeneration[J]. Telemed J E Health, 2020, 26: 565-568. DOI: 10.1089/tmj.2020.0011
|
[21] |
Hwang DK, Hsu CC, Chang KJ, et al. Artificial intelligence-based decision-making for age-related macular degeneration[J]. Theranostics, 2019, 9: 232-245. DOI: 10.7150/thno.28447
|
[22] |
Li B, Powell AM, Hooper PL, et al. Prospective evalua-tion of teleophthalmology in screening and recurrence monitoring of neovascular age-related macular degeneration: a randomized clinical trial[J]. JAMA Ophthalmol, 2015, 133: 276-282. DOI: 10.1001/jamaophthalmol.2014.5014
|
[23] |
Wittenborn JS, Clemons T, Regillo C, et al. Economic Evaluation of a Home-Based Age-Related Macular Degenera-tion Monitoring System[J]. JAMA Ophthalmol, 2017, 135: 452-459. DOI: 10.1001/jamaophthalmol.2017.0255
|
[24] |
Acemoglu A, Peretti G, Trimarchi M, et al. Operating From a Distance: Robotic Vocal Cord 5G Telesurgery on a Cadaver[J]. Ann Intern Med, 2020, 173: 940-941. DOI: 10.7326/M20-0418
|
[25] |
Sachdeva N, Klopukh M, Clair RS, et al. Using conditional generative adversarial networks to reduce the effects of latency in robotic telesurgery[J]. J Robot Surg, 2021, 15: 635-641. DOI: 10.1007/s11701-020-01149-5
|
[26] |
Zheng J, Wang Y, Zhang J, et al. 5G ultra-remote robot-assisted laparoscopic surgery in China[J]. Surg Endosc, 2020, 34: 5172-5180. DOI: 10.1007/s00464-020-07823-x
|
[27] |
Chen H, Pan X, Yang J, et al. Application of 5G Technology to Conduct Real-Time Teleretinal Laser Photocoagulation for the Treatment of Diabetic Retinopathy[J]. JAMA Ophthalmol, 2021. doi: 10.1001/jamaophthalmol.2021.2312.
|
[28] |
Annoh R, Patel S, Beck D, et al. Digital ophthalmology in Scotland: benefits to patient care and education[J]. Clin Ophthalmol, 2019, 13: 277-286. DOI: 10.2147/OPTH.S185186
|
[29] |
Tatham AJ, Ali AM, Hillier N. Knowledge of Glaucoma Among Patients Attending Virtual and Face-to-Face Glaucoma Clinics[J]. J Glaucoma, 2021, 30: 325-331. DOI: 10.1097/IJG.0000000000001758
|
[30] |
Patel AJ, Kloosterboer A, Yannuzzi NA, et al. Evaluation of the Content, Quality, and Readability of Patient Accessible Online Resources Regarding Cataracts[J]. Semin Ophthalmol, 2021, 36: 384-391. DOI: 10.1080/08820538.2021.1893758
|
[31] |
Chong JC, Tan CHN, Chen DZ. Teleophthalmology and its evolving role in a COVID-19 pandemic: A scoping review[J]. Ann Acad Med Singap, 2021, 50: 61-76. DOI: 10.47102/annals-acadmedsg.2020459
|
[32] |
Faes L, Rosenblatt A, Schwartz R, et al. Overcoming barriers of retinal care delivery during a pandemic-attitudes and drivers for the implementation of digital health: a global expert survey[J]. Br J Ophthalmol, 2020. doi: 10.1136/bjophthalmol-2020-316882.
|
[33] |
Portney DS, Zhu Z, Chen EM, et al. COVID-19 and Use of Teleophthalmology (CUT Group): Trends and Diagnoses[J]. Ophthalmology, 2021. doi: 10.1016/j.ophtha.2021.02.010.
|
[34] |
Bourdon H, Herbaut A, Trinh L, et al. An algorithm in ophthalmic emergencies to evaluate the necessity of physical consultation during COVID-19 lockdown in Paris: Experi-ence of the first 100 patients[J]. J Fr Ophtalmol, 2021, 44: 307-312. DOI: 10.1016/j.jfo.2020.12.002
|
[35] |
Ghazala FR, Hamilton R, Giardini ME, et al. Teleophthalmology techniques increase ophthalmic examination distance[J]. Eye(Lond), 2021, 6: 1780-1781. http://www.nature.com/articles/s41433-020-1085-8
|
[36] |
Sommer AC, Blumenthal EZ. Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak[J]. Graefes Arch Clin Exp Ophthalmol, 2020, 258: 2341-2352. DOI: 10.1007/s00417-020-04879-2
|
[37] |
Rathi S, Tsui E, Mehta N, et al. The Current State of Teleophthalmology in the United States[J]. Ophthalmology, 2017, 124: 1729-1734. DOI: 10.1016/j.ophtha.2017.05.026
|
[38] |
Wildenbos GA, Peute L, Jaspers M. Aging barriers influencing mobile health usability for older adults: A literature based framework (MOLD-US)[J]. Int J Med Inform, 2018, 114: 66-75. DOI: 10.1016/j.ijmedinf.2018.03.012
|
[39] |
Gioia G, Salducci M. Medical and legal aspects of telemedicine in ophthalmology[J]. Rom J Ophthalmol, 2019, 63: 197-207. DOI: 10.22336/rjo.2019.31
|
[1] | LU Yao, LIU Jianing, WANG Mian, HUANG Jiajie, HAN Baojin, SUN Mingyao, CHENG Qianji, NING Jinling, GE Long. Artificial Intelligence in Shared Decision Making[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(3): 661-667. DOI: 10.12290/xhyxzz.2023-0209 |
[2] | LAI Xinxiu, WANG Xiang. Application Status and Research Advances of Artificial Intelligence in Pancreatobiliary Endoscopy[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(2): 387-393. DOI: 10.12290/xhyxzz.2023-0524 |
[3] | MENG Xiangfeng, WANG Hao, LI Jiage. Interpretation on the Standard Artificial Intelligence Medical Device- Quality Requirements and Evaluation-Part 1: Terminology[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(6): 1175-1179. DOI: 10.12290/xhyxzz.2023-0351 |
[4] | CHEN Youxin, XU Zhiyan. Artificial Intelligence Assisted Therapeutic Regimen and Technology Transformation in Retinal Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(6): 1131-1134. DOI: 10.12290/xhyxzz.2023-0247 |
[5] | DI Yu, LI Ying. The Application and Research Progress of Artificial Intelligence in Corneal Related Diseases[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 761-767. DOI: 10.12290/xhyxzz.2020-0098 |
[6] | YU Weihong, ZHANG Xiao, WU Chan, CHEN Huan, YANG Zhikun, HE Feng, ZHANG Zhiqiao, ZHANG Bilei, GONG Di, WANG Yuelin, YANG Jingyuan, LI Bing, SUN Yanyuan, MA Yajing, LU Huiqin, XIA Wei, ZHOU Wei, ZHANG Donglei, PAN Qingmin, YANG Ning, WANG Shuna, SUN Xiaolei, YU Ying, SU Chang, WAN Bo, WANG Mingqi, WANG Min, CHEN Youxin. Procedures of Establishing a Well-annotated Database of Color Fundus Photography of Diabetic Retinopathy for Artificial Intelligence Research[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 684-688. DOI: 10.12290/xhyxzz.2021-0613 |
[7] | LI Xirong. Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 602-607. DOI: 10.12290/xhyxzz.2021-0500 |
[8] | WANG Yao, SUN Hongli, ZHAO Ying, ZHANG Li, ZHU Renyuan, DOU Hongtao, YUAN Ying, LIU Yali, LIU Wenjing, XU Yingchun. Accuracy Assessment of the Morphological Analysis System with Automated Microscopy and Artificial Intelligence for Gram-stained Vaginal Discharge Smears[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(4): 503-509. DOI: 10.12290/xhyxzz.2021-0412 |
[9] | Rui-feng LIU, Yu XIA, Yu-xin JIANG. Application of Artificial Intelligence in Ultrasound Medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(5): 453-457. DOI: 10.3969/j.issn.1674-9081.2018.05.015 |
[10] | Zheng-yu JIN. Prospects and Challenges:when Medical Imaging Meets Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 2-4. DOI: 10.3969/j.issn.1674-9081.2018.01.001 |