您的位置: 首页 > 2024年1月 第1卷 第1期 > 文字全文
2023年7月 第38卷 第7期11
目录

From barn lanterns to the 5G intelligent ophthalmic cruiser: the perspective of artificial intelligence and digital technologies on the modality and efficiency of blindness prevention in China

From barn lanterns to the 5G intelligent ophthalmic cruiser: the perspective of artificial intelligence and digital technologies on the modality and efficiency of blindness prevention in China

来源期刊: Eye Science | 2024年1月 第1卷 第1期 1-6 发布时间:2024-03-28 收稿时间:2024/1/3 11:40:36 阅读量:9027
作者:
关键词:
blindness prevention artificial intelligence telemedicine mobile clinics
blindness prevention artificial intelligence telemedicine mobile clinics
DOI:
10.12419/es23122101
Received date:
2023-12-31 
Accepted date:
2024-03-28 
Published online:
2024-03-28 

Blindness prevention has been an important national policy in China. Previous strategies, such as deploying experienced cataract surgeons to rural areas and assisting in building local ophthalmology centers, had successfully decreased the prevalence of visual impairment and blindness. However, new challenges arise with the aging population and the shift of the disease spectrum towards age-related eye diseases and myopia. With the constant technological boom, digital healthcare innovations in ophthalmology could immensely enhance screening and diagnosing capabilities. Artifcial intelligence (AI) and telemedicine have been proven valuable in clinical ophthalmology settings. Moreover, the integration of cutting-edge communication technology and AI in mobile clinics and remote surgeries is on the horizon, potentially revolutionizing blindness prevention and ophthalmic healthcare. The future of blindness prevention in China is poised to undergo signifcant transformation, driven by emerging challenges and new opportunities.

Blindness prevention has been an important national policy in China. Previous strategies, such as deploying experienced cataract surgeons to rural areas and assisting in building local ophthalmology centers, had successfully decreased the prevalence of visual impairment and blindness. However, new challenges arise with the aging population and the shift of the disease spectrum towards age-related eye diseases and myopia. With the constant technological boom, digital healthcare innovations in ophthalmology could immensely enhance screening and diagnosing capabilities. Artifcial intelligence (AI) and telemedicine have been proven valuable in clinical ophthalmology settings. Moreover, the integration of cutting-edge communication technology and AI in mobile clinics and remote surgeries is on the horizon, potentially revolutionizing blindness prevention and ophthalmic healthcare. The future of blindness prevention in China is poised to undergo signifcant transformation, driven by emerging challenges and new opportunities.

Introduction

Blindness and visual impairment is the third leading cause of disability in the world.[1] Since the founding of the People’s Republic of China, the prevention and treatment of blindness has been attached great importance.[2] The earliest practice could be dated back to the 1950s, when teams of  ophthalmologists went into the rural and coastal areas with barn lanterns to light up the dark village roads, knocking on the doors of villagers who suffered from eye diseases house by house.[3-5] It was part of the initiatives to eliminate blindness caused by cataracts, the major eye disease responsible for curable visual impairment at the time. To restore sight and improve ophthalmology capacity in remote areas, experienced ophthalmologists were sent with the task of performing cataract surgeries, as well as supporting the construction of regional eye centres and training local physicians under the call of national programs such as ‘Sight First, China Action’, ‘Lifeline Express[6], and ‘China Million Cataract Surgeries Program[7]. The results were encouraging. Epidemiologic surveys in 2006 and 2014 showed that the prevalence of visual impairment and blindness in China decreased by more than 20%,[8] and the cataract surgical rate was 7 times more than that of 1999, reaching 2,205.[9]

Despite the outstanding achievements of the series of efforts in preventing and treating blindness, the demographic and lifestyle changes of the new era are bound to bring about entirely new challenges. The 2014 Nine-Province Surveys in Rural China revealed that retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD) have escalated in prevalence and become some of the major causes of irreversible blindness.[10] The boom of myopia in the digital age has also made myopia-related complications a critical risk factor for visual impairment.[11] Since traditional approaches to blindness prevention mainly increase the treatment capacity of reversible blinding diseases, they are not as effective in tackling the changing disease spectrum. In addition, previous studies demonstrated that the number of ophthalmologists per capita is signifcantly higher in the country’s more developed eastern regions, and that the surgical capacity of county-level ophthalmic institutions is still signifcantly insufcient.[12] Such results indicated that the unequal distribution of high-quality ophthalmic resources in the country remains unresolved.

The rise of intelligent and digital technologies brings unprecedented opportunities for the global healthcare industry. Along with the development of artificial intelligence (AI), 5th generation wireless networks (5G), virtual reality (VR), and augmented reality (AR), digital medicine provides a novel solution to the quest of providing fair and accessible health care services.[13] Real-world applications of digital healthcare innovations in ophthalmology were widely reported. Tele-screening for diabetic retinopathy based on fundus photography has been proven to increase screening rates and cost-effectiveness in many countries.[14-16] The breakthrough of deep learning technology enables automatic image analysis and classifcation, leading to the rise of various screening and diagnostic models of DR,[17-18] AMD,[19] glaucoma,[20] and retinopathy of prematurity[21] based on ocular data such as fundus photos and OCT images. For eye diseases requiring long-term follow-up, virtual clinics for AMD[22-23] and glaucoma[24-25] provided a promising method for effective long-term management. VR and AR-based clinical simulation were also implemented for ophthalmologist training during the COVID-19 pandemic to mitigate exposure risks.[26-27]

The exponential growth in screening, diagnosis and treatment innovations will undoubtedly break the mould in ophthalmic healthcare. How the current technology boom could reshape the prevention of blindness would be a crucial question for China's ophthalmology medical practitioners and policymakers to ponder. The application of AI and 5G technology signifies that healthcare resources can transcend time and space, making it easier for the general public to obtain medical services and for healthcare professionals to obtain training and support. With the popularization of AI models and telemedicine, it would be feasible to implement large-scale blindness prevention projects with broader coverage. Ophthalmologists on the other side of the country will be able to knock on the door of every patient in need, regardless of location and wealth.

A recent study from Liu et al.[28] has provided evidence for emerging new technologies in real-world ophthalmic scenarios. Their results showed a higher cost-efectiveness in AI- and telemedicine-assisted screening than traditional face-to-face screening for a wide range of blinding eye diseases in urban and rural areas. It means that primary care centres can detect AMD, glaucoma, cataracts, diabetic retinopathy, and pathologic myopia at an early stage with basic ophthalmic examination equipment, reducing the chance of developing resultant irreversible blindness. The widespread 5G technology and the recent introduction of surgical robot-assisted cataract surgery,[14-16] vitreoretinal surgery,[29] and corneal transplantation[30] have also provided favourable conditions for developing remote ophthalmologic surgeries.

In recent years, many blindness prevention initiatives incorporating digital healthcare have achieved commendable results. For instance, the nationwide telemedicine-enabled DR screening programme fueled by Lifeline Express has established 29 screening centres across the country and served more than 30,000 diabetic patients from 2014 to 2016.[6] Another teleophthalmology approach in the Guangdong provinceintegrated DR and glaucoma grading with medical educationby linking the regional ophthalmological centre with multiple rural hospitals.[31] The Beijing Eye Public Health Care Project also presented a successful tele-screening schemebased onocular images, reaching over 500,000 residentsand identified cataracts, glaucoma and diabetic retinopathy as the major causes of visual impairment.[32] As for AI diagnosis programs, the Comprehensive Artificial Intelligence Retinal Expert (CARE) system has been proven effective for detecting 14 common retinal abnormalitiesbased on national real-world data.[33] These pioneering studies have provided valuable examples for applying digital health technologies in blindness prevention.

People with visual impairments often suffer from reduced mobility, making it more difficult for them to seek medical assistance actively. Mobile clinics have been shown to play an irreplaceable role for people who have difficulty accessing medical resources.[20] Together with 5G communication technology, Internet of Things (IoT) and AI models, mobile clinics can be an ideal carrier for digital healthcare. In terms of disease screening and management, vehicle-mounted instruments integrated with network connection and diagnostic models could archive remote screening, diagnosis, consultation, and follow-up on the patients’doorstep. An example of real-world application is the 5G Intelligent Ophthalmic Cruiser launched by Zhongshan Ophthalmic Center, which has served more than 10,000 people in over 20 regions over the past year. The cruiseris equipped with automatic eye examination equipment and thevehicular 5G network, allowingthe tele-screening of common eye diseases in remote areas of the country. For sight-restoring surgery, it can be expected that mobile clinics equipped with surgical robots to perform standardized operations will become a new norm in the future. Regarding medical education, the frontiers of ophthalmology could be brought to primary healthcare institutions with ease, signifcantly reducing the learning costs and enhancing the capacity of regional ophthalmic healthcare more efficiently. It could substantially improve the quality of local healthcare, leading to a more sustainable and autogenous system of blindness prevention.

The sci-tech revolution has brought fundamental changes to blindness prevention, presenting new challenges and opportunities beyond traditional methods. From barn lanterns to the 5G Intelligent Ophthalmic Cruiser, digital technologies have reshaped ophthalmic healthcare in China. The real-world application of various telemedicine solutions during the COVID-19 pandemic has accelerated the emergence of digital medicine as the new normal in ophthalmology. Reliability, ethics and morality, and patient acceptance are still pending challenges. Nevertheless, the compelling development in this new era can undoubtedly fuel a series of transformations and advances in blindness prevention in China.

Correction notice

None

Acknowledgement

None

Author Contributions

(I) Conception and design: Haotian Lin
(II)Administrative support: Haotian Lin
(III) Provision of study materials or patients: Haotian Lin, Wei Xiao
(IV) Collection and assembly of data: Wei Xiao, Wai Cheng Iao
(V) Data analysis and interpretation: Wei Xiao, Wai Cheng Iao
(VI) Manuscript writing:All authors
(VII) Final approval of manuscript:All authors

Funding

This work was supported by the Science and Technology Program of Guangzhou (202201020337), the Science and Technology Planning Projects of Guangdong Province (2021B1111610006), the Science and Technology Program of Guangzhou (2024B03J1233), the National Natural Science Foundation of China (82171035), the High-level Science and Technology Journals Projects of Guangdong Province (2021B1212010003), the National Natural Science Foundation of China (82201237), the China Postdoctoral Science Foundation (2023T160751).

The funding organizations had no role in the following aspects: design and conduct of the study; the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and the decision to submit the manuscript for publication.

ConfictofInterests

None of the authors has any conflicts of interest to disclose. All authors have declared in the completed the ICMJE uniform disclosure form.

Patient consent for publication

None

Ethical Statement

This study does not contain any studies with human or animal subjects performed by any of the authors

Provenance and PeerReview

This article was a standard submission to our journal. The article has undergone peer review with ouranonymous review system

Data Sharing Statement

None

OpenAccess Statement

This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
1、Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). Nov 10 2018;392(10159):1789-1858. doi:10.1016/s0140-6736(18)32279-7Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (London, England). Nov 10 2018;392(10159):1789-1858. doi:10.1016/s0140-6736(18)32279-7
2、Hu C. Prevention of blindness in China. Chinese medical journal. Aug 1992;105(8):695-8.Hu C. Prevention of blindness in China. Chinese medical journal. Aug 1992;105(8):695-8.
3、Wei ZX. [The methodology and focus in blindness prevention from an epidemiologic point of view]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Nov 1989;25(6):365-7.Wei ZX. [The methodology and focus in blindness prevention from an epidemiologic point of view]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Nov 1989;25(6):365-7.
4、Zhao JL. [Prevention of blindness is still an arduous task and embarks on a long road in China]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Mar 2012;48(3):193-5.Zhao JL. [Prevention of blindness is still an arduous task and embarks on a long road in China]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Mar 2012;48(3):193-5.
5、Wei WB, Zhu RL, Yang L. Situation of low vision and blindness in China and their prevention. Chinese medical journal. Apr 2011;124(8):1123-7.Wei WB, Zhu RL, Yang L. Situation of low vision and blindness in China and their prevention. Chinese medical journal. Apr 2011;124(8):1123-7.
6、Wong IYH, Ni MY, Wong IOL, Fong N, Leung GM. Saving sight in China and beyond: the Lifeline Express model. BMJ Global Health. 2018;3(4)doi:10.1136/bmjgh-2018-000766.Wong IYH, Ni MY, Wong IOL, Fong N, Leung GM. Saving sight in China and beyond: the Lifeline Express model. BMJ Global Health. 2018;3(4)doi:10.1136/bmjgh-2018-000766.
7、Yan X, Guan C, Mueller A, et al. Outcomes and Projected Impact on Vision Restoration of the China Million Cataract Surgeries Program. Ophthalmic Epidemiology. 2013;20(5):294-300. doi:10.3109/09286586.2013.821136Yan X, Guan C, Mueller A, et al. Outcomes and Projected Impact on Vision Restoration of the China Million Cataract Surgeries Program. Ophthalmic Epidemiology. 2013;20(5):294-300. doi:10.3109/09286586.2013.821136
8、Zhao J, Xu X, Ellwein LB, et al. Prevalence of Vision Impairment in Older Adults in Rural China in 2014 and Comparisons With the 2006 China Nine-Province Survey. American Journal of Ophthalmology. 2018;185:81-93. doi:10.1016/j.ajo.2017.10.016.Zhao J, Xu X, Ellwein LB, et al. Prevalence of Vision Impairment in Older Adults in Rural China in 2014 and Comparisons With the 2006 China Nine-Province Survey. American Journal of Ophthalmology. 2018;185:81-93. doi:10.1016/j.ajo.2017.10.016.
9、Mayinuer Y, Wang NL. [Vision 2020: the progress of blindness prevention and eye health in China]. Zhonghua yi xue za zhi. Dec 29 2020;100(48):3831-3834. doi:10.3760/cma.j.cn112137-20200825-02468.Mayinuer Y, Wang NL. [Vision 2020: the progress of blindness prevention and eye health in China]. Zhonghua yi xue za zhi. Dec 29 2020;100(48):3831-3834. doi:10.3760/cma.j.cn112137-20200825-02468.
10、Zhao J, Xu X, Ellwein LB, et al. Causes of Visual Impairment and Blindness in the 2006 and 2014 Nine-Province Surveys in Rural China. American Journal of Ophthalmology. 2019;197:80-87. doi:10.1016/j.ajo.2018.09.011.Zhao J, Xu X, Ellwein LB, et al. Causes of Visual Impairment and Blindness in the 2006 and 2014 Nine-Province Surveys in Rural China. American Journal of Ophthalmology. 2019;197:80-87. doi:10.1016/j.ajo.2018.09.011.
11、Wu P-C, Huang H-M, Yu H-J, Fang P-C, Chen C-T. Epidemiology of Myopia. Asia-Pacific Journal of Ophthalmology. 2016;5(6):386-393. doi:10.1097/apo.0000000000000236.Wu P-C, Huang H-M, Yu H-J, Fang P-C, Chen C-T. Epidemiology of Myopia. Asia-Pacific Journal of Ophthalmology. 2016;5(6):386-393. doi:10.1097/apo.0000000000000236.
12、Feng JJ, An L, Wang ZF, Zhan LL, Xu X. [Analysis on ophthalmic human resource allocation and service delivery at county level in Mainland China in 2014]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Dec 11 2018;54(12):929-934. doi:10.3760/cma.j.issn.0412-4081.2018.12.011.Feng JJ, An L, Wang ZF, Zhan LL, Xu X. [Analysis on ophthalmic human resource allocation and service delivery at county level in Mainland China in 2014]. [Zhonghua yan ke za zhi] Chinese journal of ophthalmology. Dec 11 2018;54(12):929-934. doi:10.3760/cma.j.issn.0412-4081.2018.12.011.
13、Li J-PO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Progress in Retinal and Eye Research. 2021;82doi:10.1016/j.preteyeres.2020.100900.Li J-PO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Progress in Retinal and Eye Research. 2021;82doi:10.1016/j.preteyeres.2020.100900.
14、Nguyen HV, Tan GSW, Tapp RJ, et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 2016;123(12):2571-2580. doi:10.1016/j.ophtha.2016.08.021.Nguyen HV, Tan GSW, Tapp RJ, et al. Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore. Ophthalmology. 2016;123(12):2571-2580. doi:10.1016/j.ophtha.2016.08.021.
15、Scotland GS, McNamee P, Fleming AD, et al. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. British Journal of Ophthalmology. 2009;94(6):712-719. doi:10.1136/bjo.2008.151126.Scotland GS, McNamee P, Fleming AD, et al. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. British Journal of Ophthalmology. 2009;94(6):712-719. doi:10.1136/bjo.2008.151126.
16、Newman Casey PA. Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs. Journal of Endocrinology and Diabetes. 2015;2(4):01-10. doi:10.15226/2374-6890/2/4/00131.Newman Casey PA. Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs. Journal of Endocrinology and Diabetes. 2015;2(4):01-10. doi:10.15226/2374-6890/2/4/00131.
17、Abràmoff MD, Lou Y, Erginay A, et al. Improved Automated Detection of Diabetic Retinopathy on a Publicly Ava ilable Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016/10/1/ 57(13):5200-5206. doi:10.1167/iovs.16-19964.Abràmoff MD, Lou Y, Erginay A, et al. Improved Automated Detection of Diabetic Retinopathy on a Publicly Ava ilable Dataset Through Integration of Deep Learning. Invest Ophthalmol Vis Sci. 2016/10/1/ 57(13):5200-5206. doi:10.1167/iovs.16-19964.
18、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. JAMA. 2016/12/13/ 316(22):2402-2410. doi:10.1001/jama.2016.17216.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. JAMA. 2016/12/13/ 316(22):2402-2410. doi:10.1001/jama.2016.17216.
19、Grassmann F, Mengelkamp J, Brandl C, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease St udy Severity Scale for Age-Related Macular Degeneration from Color Fun dus Photography. Ophthalmology. 2018/9// 125(9):1410-1420. doi:10.1016/j.ophtha.2018.02.037.Grassmann F, Mengelkamp J, Brandl C, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease St udy Severity Scale for Age-Related Macular Degeneration from Color Fun dus Photography. Ophthalmology. 2018/9// 125(9):1410-1420. doi:10.1016/j.ophtha.2018.02.037.
20、Liu H, Li L, Wormstone IM, et al. Development and Validation of a Deep Learning System to Detect Glaucom atous Optic Neuropathy Using Fundus Photographs. JAMA Ophthalmol. 2019/12/1/ 137(12):1353-1360. doi:10.1001/jamaophthalmol.2019.3501.Liu H, Li L, Wormstone IM, et al. Development and Validation of a Deep Learning System to Detect Glaucom atous Optic Neuropathy Using Fundus Photographs. JAMA Ophthalmol. 2019/12/1/ 137(12):1353-1360. doi:10.1001/jamaophthalmol.2019.3501.
21、Brown JM, Campbell JP, Beers A, et al. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Usin g Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018/7/1/ 136(7):803-810. doi:10.1001/jamaophthalmol.2018.1934.Brown JM, Campbell JP, Beers A, et al. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Usin g Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018/7/1/ 136(7):803-810. doi:10.1001/jamaophthalmol.2018.1934.
22、Armstrong GW, Miller JB. Telemedicine for the Diagnosis and Management of Age-Related Macular Degeneration: A Review. Journal of clinical medicine. Feb 5 2022;11(3)doi:10.3390/jcm11030835.Armstrong GW, Miller JB. Telemedicine for the Diagnosis and Management of Age-Related Macular Degeneration: A Review. Journal of clinical medicine. Feb 5 2022;11(3)doi:10.3390/jcm11030835.
23、Tsaousis KT, Empeslidis T, Konidaris VE, Kapoor B, Deane J. The concept of virtual clinics in monitoring patients with age-related macular degeneration. Acta ophthalmologica. Aug 2016;94(5):e353-5. doi:10.1111/aos.12832.Tsaousis KT, Empeslidis T, Konidaris VE, Kapoor B, Deane J. The concept of virtual clinics in monitoring patients with age-related macular degeneration. Acta ophthalmologica. Aug 2016;94(5):e353-5. doi:10.1111/aos.12832.
24、Lee YF, Chay J, Husain R, et al. Three-year Outcomes of an Expanded Asynchronous Virtual Glaucoma Clinic in Singapore. Asia-Pacific journal of ophthalmology (Philadelphia, Pa). Jul-Aug 01 2023;12(4):364-369. doi:10.1097/apo.0000000000000620.Lee YF, Chay J, Husain R, et al. Three-year Outcomes of an Expanded Asynchronous Virtual Glaucoma Clinic in Singapore. Asia-Pacific journal of ophthalmology (Philadelphia, Pa). Jul-Aug 01 2023;12(4):364-369. doi:10.1097/apo.0000000000000620.
25、Gunn PJG, Marks JR, Au L, Waterman H, Spry PGD, Harper RA. Acceptability and use of glaucoma virtual clinics in the UK: a national survey of clinical leads. BMJ open ophthalmology. 2018;3(1):e000127. doi:10.1136/bmjophth-2017-000127.Gunn PJG, Marks JR, Au L, Waterman H, Spry PGD, Harper RA. Acceptability and use of glaucoma virtual clinics in the UK: a national survey of clinical leads. BMJ open ophthalmology. 2018;3(1):e000127. doi:10.1136/bmjophth-2017-000127.
26、Mishra K, Boland MV, Woreta FA. Incorporating a virtual curriculum into ophthalmology education in the coronavirus disease-2019 era. Current opinion in ophthalmology. Sep 2020;31(5):380-385. doi:10.1097/icu.0000000000000681.Mishra K, Boland MV, Woreta FA. Incorporating a virtual curriculum into ophthalmology education in the coronavirus disease-2019 era. Current opinion in ophthalmology. Sep 2020;31(5):380-385. doi:10.1097/icu.0000000000000681.
27、Iskander M, Ogunsola T, Ramachandran R, McGowan R, Al-Aswad LA. Virtual Reality and Augmented Reality in Ophthalmology: A Contemporary Prospective. Asia-Pacific journal of ophthalmology (Philadelphia, Pa). May-Jun 01 2021;10(3):244-252. doi:10.1097/apo.0000000000000409.Iskander M, Ogunsola T, Ramachandran R, McGowan R, Al-Aswad LA. Virtual Reality and Augmented Reality in Ophthalmology: A Contemporary Prospective. Asia-Pacific journal of ophthalmology (Philadelphia, Pa). May-Jun 01 2021;10(3):244-252. doi:10.1097/apo.0000000000000409.
28、Liu H, Li R, Zhang Y, et al. Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis. The Lancet Global Health. 2023;11(3):e456-e465. doi:10.1016/s2214-109x(22)00554-x.Liu H, Li R, Zhang Y, et al. Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis. The Lancet Global Health. 2023;11(3):e456-e465. doi:10.1016/s2214-109x(22)00554-x.
29、Channa R, Iordachita I, Handa JT. Robotic Vitreoretinal Surgery. Retina (Philadelphia, Pa). Jul 2017;37(7):1220-1228. doi:10.1097/iae.0000000000001398.Channa R, Iordachita I, Handa JT. Robotic Vitreoretinal Surgery. Retina (Philadelphia, Pa). Jul 2017;37(7):1220-1228. doi:10.1097/iae.0000000000001398.
30、Savastano A, Rizzo S. A Novel Microsurgical Robot: Preliminary Feasibility Test in Ophthalmic Field. Translational vision science & technology. Aug 1 2022;11(8):13. doi:10.1167/tvst.11.8.13.Savastano A, Rizzo S. A Novel Microsurgical Robot: Preliminary Feasibility Test in Ophthalmic Field. Translational vision science & technology. Aug 1 2022;11(8):13. doi:10.1167/tvst.11.8.13.
1、This study was funded by the Science and Technology Program of Guangzhou(No. 202201020337)
2、This study was funded by the Science and Technology Planning Projects of Guangdong Province(No. 2021B1111610006)
3、This study was funded by the Science and Technology Program of Guangzhou(No. 2024B03J1233)
4、This study was funded by the National Natural Science Foundation of China(No. 82171035)
5、This study was funded by the High-level Science and Technology Journals Projects of Guangdong Province(No. 2021B1212010003)
6、This study was funded by the National Natural Science Foundation of China(No. 82201237)
7、This study was funded by the China Postdoctoral Science Foundation(No. 2023T160751)
上一篇
下一篇
出版者信息
目录