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Liu Z, Han X, Gao L, Chen S, Huang W, Li P, Wu Z, Wang M, Zheng Y. Cost-effectiveness of incorporating self-imaging optical coherence tomography into fundus photography-based diabetic retinopathy screening. NPJ Digit Med 2024; 7:225. [PMID: 39181938 PMCID: PMC11344775 DOI: 10.1038/s41746-024-01222-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
Diabetic macular edema (DME) has emerged as the foremost cause of vision loss in the population with diabetes. Early detection of DME is paramount, yet the prevailing screening, relying on two-dimensional and labor-intensive fundus photography (FP), results in frequent unwarranted referrals and overlooked diagnoses. Self-imaging optical coherence tomography (SI-OCT), offering fully automated, three-dimensional macular imaging, holds the potential to enhance DR screening. We conducted an observational study within a cohort of 1822 participants with diabetes, who received comprehensive assessments, including visual acuity testing, FP, and SI-OCT examinations. We compared the performance of three screening strategies: the conventional FP-based strategy, a combination strategy of FP and SI-OCT, and a simulated combination strategy of FP and manual SD-OCT. Additionally, we undertook a cost-effectiveness analysis utilizing Markov models to evaluate the costs and benefits of the three strategies for referable DR. We found that the FP + SI-OCT strategy demonstrated superior sensitivity (87.69% vs 61.53%) and specificity (98.29% vs 92.47%) in detecting DME when compared to the FP-based strategy. Importantly, the FP + SI-OCT strategy outperformed the FP-based strategy, with an incremental cost-effectiveness ratio (ICER) of $8016 per quality-adjusted life year (QALY), while the FP + SD-OCT strategy was less cost-effective, with an ICER of $45,754/QALY. Our results were robust to extensive sensitivity analyses, with the FP + SI-OCT strategy standing as the dominant choice in 69.36% of simulations conducted at the current willingness-to-pay threshold. In summary, incorporating SI-OCT into FP-based screening offers substantial enhancements in sensitivity, specificity for detecting DME, and most notably, cost-effectiveness for DR screening.
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Affiliation(s)
- Zitian Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
| | - Xiaotong Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Le Gao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Shida Chen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Peng Li
- MOPTIM Imaging Technique Co. Ltd, Shenzhen, China
| | - Zhiyan Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Mengchi Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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2
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Wu X, Wu Y, Tu Z, Cao Z, Xu M, Xiang Y, Lin D, Jin L, Zhao L, Zhang Y, Liu Y, Yan P, Hu W, Liu J, Liu L, Wang X, Wang R, Chen J, Xiao W, Shang Y, Xie P, Wang D, Zhang X, Dongye M, Wang C, Ting DSW, Liu Y, Pan R, Lin H. Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China. Nat Commun 2024; 15:3650. [PMID: 38688925 PMCID: PMC11061155 DOI: 10.1038/s41467-024-47211-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/25/2024] [Indexed: 05/02/2024] Open
Abstract
Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.
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Affiliation(s)
- Xiaohang Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuxuan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Zhenjun Tu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Zizheng Cao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Miaohong Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yifan Xiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Ling Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yingzhe Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Yu Liu
- School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Pisong Yan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Weiling Hu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Jiali Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Lixue Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Xun Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Ruixin Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Jieying Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Wei Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Yuanjun Shang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Peichen Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Dongni Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Xulin Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Meimei Dongye
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Chenxinqi Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Yizhi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
| | - Rong Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China.
- Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, Hainan, China.
- Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China.
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Zhang J, Luo X, Li D, Peng Y, Gao G, Lei L, Gao M, Lu L, Xu Y, Yu T, Lin S, Ma Y, Yao C, Zou H. Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting. Biomed Eng Online 2024; 23:32. [PMID: 38475784 DOI: 10.1186/s12938-024-01222-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN Prospective cross-sectional study. METHODS In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.
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Affiliation(s)
- Juzhao Zhang
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuan Luo
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Deshang Li
- Sijing Community Health Service Center, Shanghai, China
| | - Yajun Peng
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Guiling Gao
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Liangwen Lei
- Sijing Community Health Service Center, Shanghai, China
| | - Meng Gao
- Sijing Community Health Service Center, Shanghai, China
| | - Lina Lu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yi Xu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Tao Yu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Senlin Lin
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| | - Yingyan Ma
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunxia Yao
- Songjiang Disease Control and Prevention Center, Shanghai, China.
| | - Haidong Zou
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Li Q, Tan J, Xie H, Zhang X, Dai Q, Li Z, Yan LL, Chen W. Evaluating the accuracy of the Ophthalmologist Robot for multiple blindness-causing eye diseases: a multicentre, prospective study protocol. BMJ Open 2024; 14:e077859. [PMID: 38431298 PMCID: PMC10910653 DOI: 10.1136/bmjopen-2023-077859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 01/12/2024] [Indexed: 03/05/2024] Open
Abstract
INTRODUCTION Early eye screening and treatment can reduce the incidence of blindness by detecting and addressing eye diseases at an early stage. The Ophthalmologist Robot is an automated device that can simultaneously capture ocular surface and fundus images without the need for ophthalmologists, making it highly suitable for primary application. However, the accuracy of the device's screening capabilities requires further validation. This study aims to evaluate and compare the screening accuracies of ophthalmologists and deep learning models using images captured by the Ophthalmologist Robot, in order to identify a screening method that is both highly accurate and cost-effective. Our findings may provide valuable insights into the potential applications of remote eye screening. METHODS AND ANALYSIS This is a multicentre, prospective study that will recruit approximately 1578 participants from 3 hospitals. All participants will undergo ocular surface and fundus images taken by the Ophthalmologist Robot. Additionally, 695 participants will have their ocular surface imaged with a slit lamp. Relevant information from outpatient medical records will be collected. The primary objective is to evaluate the accuracy of ophthalmologists' screening for multiple blindness-causing eye diseases using device images through receiver operating characteristic curve analysis. The targeted diseases include keratitis, corneal scar, cataract, diabetic retinopathy, age-related macular degeneration, glaucomatous optic neuropathy and pathological myopia. The secondary objective is to assess the accuracy of deep learning models in disease screening. Furthermore, the study aims to compare the consistency between the Ophthalmologist Robot and the slit lamp in screening for keratitis and corneal scar using the Kappa test. Additionally, the cost-effectiveness of three eye screening methods, based on non-telemedicine screening, ophthalmologist-telemedicine screening and artificial intelligence-telemedicine screening, will be assessed by constructing Markov models. ETHICS AND DISSEMINATION The study has obtained approval from the ethics committee of the Ophthalmology and Optometry Hospital of Wenzhou Medical University (reference: 2023-026 K-21-01). This work will be disseminated by peer-review publications, abstract presentations at national and international conferences and data sharing with other researchers. TRIAL REGISTRATION NUMBER ChiCTR2300070082.
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Affiliation(s)
- Qixin Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Jie Tan
- Global Health Research Center, Duke Kunshan University, Kunshan, China
- School of Public Health, Wuhan University, Wuhan, China
| | - He Xie
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xiaoyu Zhang
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, China
| | - Qi Dai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Lijing L Yan
- Global Health Research Center, Duke Kunshan University, Kunshan, China
- School of Public Health, Wuhan University, Wuhan, China
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
- Peking University Institute for Global Health and Development, Peking University, Beijing, China
| | - Wei Chen
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
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5
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Gu C, Wang Y, Jiang Y, Xu F, Wang S, Liu R, Yuan W, Abudureyimu N, Wang Y, Lu Y, Li X, Wu T, Dong L, Chen Y, Wang B, Zhang Y, Wei WB, Qiu Q, Zheng Z, Liu D, Chen J. Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases. Br J Ophthalmol 2024; 108:424-431. [PMID: 36878715 PMCID: PMC10894824 DOI: 10.1136/bjo-2022-322940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 02/19/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND/AIMS This study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS. METHODS This real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated. RESULTS A total of 4795 participants were included. The median age was 57.0 (IQR 39.0-66.0) years, and 3175 (66.2%) participants were female. The accuracy, specificity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai. CONCLUSION This study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance. TRIAL REGISTRATION NUMBER NCT04592068.
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Affiliation(s)
- Chufeng Gu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Eye Diseases; Key Laboratory of Ocular Fundus Diseases; Engineering Center for Visual Science and Photomedicine; Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yujie Wang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Eye Diseases; Key Laboratory of Ocular Fundus Diseases; Engineering Center for Visual Science and Photomedicine; Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yan Jiang
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
| | - Feiping Xu
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
| | - Shasha Wang
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
| | - Rui Liu
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
| | - Wen Yuan
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
| | - Nurbiyimu Abudureyimu
- Department of Ophthalmology, Bachu County Traditional Chinese Medicine Hospital of Kashgar, Xinjiang, China
| | - Ying Wang
- Department of Ophthalmology, Bachu Country People's Hospital of Kashgar, Xinjiang, China
| | - Yulan Lu
- Department of Ophthalmology, Linfen Community Health Service Center of Jing'an District, Shanghai, China
| | - Xiaolong Li
- Department of Ophthalmology, Pengpu New Village Community Health Service Center of Jing'an District, Shanghai, China
| | - Tao Wu
- Department of Ophthalmology, Pengpu Town Community Health Service Center of Jing'an District, Shanghai, China
| | - Li Dong
- Beijing Tongren Eye Center, Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Capital Medical University, Beijing, China
| | - Yuzhong Chen
- Beijing Airdoc Technology Co., Ltd, Beijing, China
| | - Bin Wang
- Beijing Airdoc Technology Co., Ltd, Beijing, China
| | | | - Wen Bin Wei
- Beijing Tongren Eye Center, Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Capital Medical University, Beijing, China
| | - Qinghua Qiu
- Department of Ophthalmology, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Zheng
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine; National Clinical Research Center for Eye Diseases; Key Laboratory of Ocular Fundus Diseases; Engineering Center for Visual Science and Photomedicine; Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Deng Liu
- Bachu Country People's Hospital of Kashgar, Xinjiang, China
- Shanghai No. 3 Rehabilitation Hospital, Shanghai, China
| | - Jili Chen
- Department of Ophthalmology, Shibei Hospital of Jing'an District, Shanghai, China
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Wang Y, Liu C, Hu W, Luo L, Shi D, Zhang J, Yin Q, Zhang L, Han X, He M. Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case. NPJ Digit Med 2024; 7:43. [PMID: 38383738 PMCID: PMC10881978 DOI: 10.1038/s41746-024-01032-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/05/2024] [Indexed: 02/23/2024] Open
Abstract
Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI's sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI's sensitivity.
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Affiliation(s)
- Yueye Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chi Liu
- Faculty of Data Science, City University of Macau, Macao SAR, China
| | - Wenyi Hu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
| | - Lixia Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Jian Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Qiuxia Yin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lei Zhang
- Clinical Medical Research Center, Children's Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210008, China.
- Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia.
- Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia.
| | - Xiaotong Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Shatin, Hong Kong.
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7
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Li R, Zhang K, Lu Z, Mou D, Wang J, Li H, Fan S, Wang N, Liu H. Cost-utility analysis of commonly used anti-glaucoma interventions for mild-to-moderate primary open-angle glaucoma patients in rural and urban China. BMJ Open 2023; 13:e073219. [PMID: 37673456 PMCID: PMC10496665 DOI: 10.1136/bmjopen-2023-073219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/20/2023] [Indexed: 09/08/2023] Open
Abstract
OBJECTIVE An increasing number of studies have explored the clinical effects of antiglaucoma surgical procedures; however, economic evidence was scarce. We aimed to compare the cost-effectiveness between maximal medical treatment (MMT) and commonly used surgical procedures (trabeculectomy, Ahmed glaucoma valve implantation, gonioscopy-assisted transluminal trabeculotomy and ab interno canaloplasty). DESIGN AND SETTING A Markov model study. PARTICIPANTS A hypothetical cohort of 100 000 patients with mild-to-moderate primary open-angle glaucoma (POAG). OUTCOMES Data were obtained from public sources. The main outcomes were incremental cost-utility ratios (ICURs) using quality-adjusted life-years (QALYs). Sensitivity analyses were conducted to verify the robustness and sensitivity of base-case results. MAIN RESULTS Both cumulative costs and QALYs gained from surgical procedures (US$6045-US$13 598, 3.33-6.05 QALYs) were higher than those from MMT (US$3117-US$6458, 3.14-5.66 QALYs). Compared with MMT, all surgical procedures satisfied the cost-effectiveness threshold (lower than US$30 501 and US$41 568 per QALY gained in rural and urban settings, respectively). During the 5-year period, trabeculectomy produced the lowest ICUR (US$21 462 and US$15 242 per QALY gained in rural and urban settings, respectively). During the 10-year-follow-up, trabeculectomy still produced the lowest ICUR (US$13 379 per QALY gained) in urban setting; however, gonioscopy-assisted transluminal trabeculotomy (US$19 619 per QALY gained) and ab interno canaloplasty (US$18 003 per QALY gained) produced lower ICURs than trabeculectomy (US$19 675 per QALY gained) in rural areas. Base-case results were most sensitive to the utilities and costs of initial treatment and maintenance. CONCLUSIONS The long-term cost-effectiveness of commonly used surgical procedures could be better than the short-term cost-effectiveness for mild-to-moderate POAG patients in China. Health economic studies, supported by more rigorous structured real-world data, are needed to assess their everyday cost-effectiveness.
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Affiliation(s)
- Ruyue Li
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Kaiwen Zhang
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Zhecheng Lu
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Dapeng Mou
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Jin Wang
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Huiqi Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Sujie Fan
- Handan City Eye Hospital, Handan, China
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
| | - Hanruo Liu
- Beijing Institute of Ophthalmology, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing, China
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8
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Dolar-Szczasny J, Barańska A, Rejdak R. Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review. J Clin Med 2023; 12:jcm12093161. [PMID: 37176602 PMCID: PMC10179149 DOI: 10.3390/jcm12093161] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
Technological advancement has brought commendable changes in medicine, advancing diagnosis, treatment, and interventions. Telemedicine has been adopted by various subspecialties including ophthalmology. Over the years, teleophthalmology has been implemented in various countries, and continuous progress is being made in this area. In underserved populations, due to socioeconomic factors, there is little or no access to healthcare facilities, and people are at higher risk of eye diseases and vision impairment. Transportation is the major hurdle for these people in obtaining access to eye care in the main hospitals. There is a dire need for accessible eye care for such populations, and teleophthalmology is the ray of hope for providing eye care facilities to underserved people. Numerous studies have reported the advantages of teleophthalmology for rural populations such as being cost-effective, timesaving, reliable, efficient, and satisfactory for patients. Although it is being practiced in urban populations, for rural populations, its benefits amplify. However, there are certain obstacles as well, such as the cost of equipment, lack of steady electricity and internet supply in rural areas, and the attitude of people in certain regions toward acceptance of teleophthalmology. In this review, we have discussed in detail eye health in rural populations, teleophthalmology, and its effectiveness in rural populations of different countries.
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Affiliation(s)
- Joanna Dolar-Szczasny
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
| | - Agnieszka Barańska
- Department of Medical Informatics and Statistics with E-Learning Laboratory, Medical University of Lublin, 20-090 Lublin, Poland
| | - Robert Rejdak
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
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9
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Lin S, Ma Y, Xu Y, Lu L, He J, Zhu J, Peng Y, Yu T, Congdon N, Zou H. Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data. JMIR Public Health Surveill 2023; 9:e41624. [PMID: 36821353 PMCID: PMC9999255 DOI: 10.2196/41624] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/13/2022] [Accepted: 01/12/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)-based and manual grading-based telemedicine screening is inadequate for policy making. OBJECTIVE The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. METHODS We conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita. RESULTS The total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy. CONCLUSIONS Our study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.
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Affiliation(s)
- Senlin Lin
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Yingyan Ma
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Yi Xu
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Lina Lu
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Jiangnan He
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Jianfeng Zhu
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Yajun Peng
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Tao Yu
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Nathan Congdon
- Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom.,Orbis International, New York, NY, United States.,Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haidong Zou
- Department of Eye Disease Prevention and Control, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
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10
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Li C, Zhu B, Zhang J, Guan P, Zhang G, Yu H, Yang X, Liu L. Epidemiology, health policy and public health implications of visual impairment and age-related eye diseases in mainland China. Front Public Health 2022; 10:966006. [PMID: 36438305 PMCID: PMC9682104 DOI: 10.3389/fpubh.2022.966006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/26/2022] [Indexed: 11/10/2022] Open
Abstract
The prevalence of visual impairment (VI) and age-related eye diseases has increased dramatically with the growing aging population in mainland China. However, there is limited comprehensive evidence on the progress of ophthalmic epidemiological research in mainland China to enhance our awareness of the prevention of eye diseases to inform public health policy. Here, we conducted a literature review of the population-based epidemiology of VI and age-related eye diseases in mainland China from the 1st of January 1946 to the 20th of October 2021. No language restrictions were applied. There was significant demographic and geographic variation in the epidemic of VI and age-related eye diseases. There are several factors known to be correlated to VI and age-related eye diseases, including age, gender, family history, lifestyle, biological factors, and environmental exposures; however, evidence relating to genetic predisposition remains unclear. In addition, posterior segment eye diseases, including age-related macular degeneration and diabetic retinopathy, are amongst the major causes of irreversible visual impairments in the senile Chinese population. There remains a significant prevention gap, with only a few individuals showing awareness and achieving optimal medical care with regards to age-related eye diseases. Multiple challenges and obstacles need to be overcome, including the accelerated aging of the Chinese population, the lack of structured care delivery in many underdeveloped regions, and unequal access to care. Despite the progress to date, there are few well-conducted multi-center population-based studies following a single protocol in mainland China, which findings can hopefully provide valuable cues for governmental decision-making and assist in addressing and halting the incidence of VI and age-related eye diseases in China.
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Affiliation(s)
- Cong Li
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,School of Medicine, South China University of Technology, Guangzhou, China
| | - Bo Zhu
- Cancer Hospital of China Medical University/Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Jie Zhang
- Department of Retina, Weifang Eye Hospital, Weifang, China
| | - Peng Guan
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China
| | - Guisen Zhang
- Department of Retina, Inner Mongolia Chaoju Eye Hospital, Hohhot, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,*Correspondence: Honghua Yu
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Xiaohong Yang
| | - Lei Liu
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China,Lei Liu
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11
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Lin S, Li L, Zou H, Xu Y, Lu L. Medical Staff and Resident Preferences for Using Deep Learning in Eye Disease Screening: Discrete Choice Experiment. J Med Internet Res 2022; 24:e40249. [PMID: 36125854 PMCID: PMC9533207 DOI: 10.2196/40249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/08/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022] Open
Abstract
Background Deep learning–assisted eye disease diagnosis technology is increasingly applied in eye disease screening. However, no research has suggested the prerequisites for health care service providers and residents willing to use it. Objective The aim of this paper is to reveal the preferences of health care service providers and residents for using artificial intelligence (AI) in community-based eye disease screening, particularly their preference for accuracy. Methods Discrete choice experiments for health care providers and residents were conducted in Shanghai, China. In total, 34 medical institutions with adequate AI-assisted screening experience participated. A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences. Results Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90% (odds ratio [OR]=0.61 for 10% overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents’ preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors’ supervision (OR=0.24; P<.001). Conclusions Deep learning model under doctors’ supervision is strongly recommended, and the specificity of the model should be more than 90%. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents.
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Affiliation(s)
- Senlin Lin
- Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Liping Li
- Shanghai Hongkou Center for Disease Control and Prevention, Shanghai, China
| | - Haidong Zou
- Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Yi Xu
- Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
| | - Lina Lu
- Shanghai Eye Disease Prevention and Treatment Center, Shanghai Eye Hospital, Shanghai, China.,Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
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