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Jin K, Li Y, Wu H, Tham YC, Koh V, Zhao Y, Kawasaki R, Grzybowski A, Ye J. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2024; 4:120-127. [PMID: 38846624 PMCID: PMC11154117 DOI: 10.1016/j.aopr.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 02/23/2024] [Accepted: 03/24/2024] [Indexed: 06/09/2024]
Abstract
Background The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.
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Affiliation(s)
- Kai Jin
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yingyu Li
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Hongkang Wu
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Victor Koh
- Centre for Innovation and Precision Eye Health, National University of Singapore, Singapore
- Department of Ophthalmology, National University of Singapore, Singapore
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Ningbo Eye Hospital, Ningbo, China
- Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Ryo Kawasaki
- Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine; Zhejiang Provincial Key Laboratory of Ophthalmology; Zhejiang Provincial Clinical Research Center for Eye Diseases; Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, China
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Chikumba S, Hu Y, Luo J. Deep learning-based fundus image analysis for cardiovascular disease: a review. Ther Adv Chronic Dis 2023; 14:20406223231209895. [PMID: 38028950 PMCID: PMC10657535 DOI: 10.1177/20406223231209895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.
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Affiliation(s)
- Symon Chikumba
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- Department of Optometry, Faculty of Healthy Sciences, Mzuzu University, Luwinga, Mzuzu, Malawi
| | - Yuqian Hu
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jing Luo
- Department of Ophthalmology, The Second Xiangya Hospital of Central South University, 139 Middle Renmin RD, Changsha, Hunan, China
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Li X, Tan TE, Wong TY, Sun X. Diabetic retinopathy in China: Epidemiology, screening and treatment trends-A review. Clin Exp Ophthalmol 2023; 51:607-626. [PMID: 37381613 DOI: 10.1111/ceo.14269] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/26/2023] [Accepted: 06/02/2023] [Indexed: 06/30/2023]
Abstract
Diabetic retinopathy (DR) is the leading cause of vision impairment in the global working-age population. In China, with one-third of the world's diabetes population estimated at 141 million, the blindness prevalence due to DR has increased significantly. The country's geographic variations in socioeconomic status have led to prominent disparities in DR prevalence, screening and management. Reported risk factors for DR in China include the classic ones, such as long diabetes duration, hyperglycaemia, hypertension and rural habitats. There is no national-level DR screening programme in China, but significant pilot efforts are underway for screening innovations. Novel agents with longer durations, noninvasive delivery or multi-target are undergoing clinical trials in China. Although optimised medical insurance policies have enhanced accessibility for expensive therapies like anti-VEGF drugs, further efforts in DR prevention and management in China are required to establish nationwide cost-effective screening programmes, including telemedicine and AI-based solutions, and to improve insurance coverage for related out-of-pocket expenses.
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Affiliation(s)
- Xiaorong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Tien-En Tan
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai, China
- Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, China
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4
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Paul W, Burlina P, Mocharla R, Joshi N, Li Z, Gu S, Nanegrungsunk O, Lin K, Bressler SB, Cai CX, Kong J, Liu TYA, Moini H, Du W, Amer F, Chu K, Vitti R, Sepehrband F, Bressler NM. Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema. JAMA Ophthalmol 2023; 141:677-685. [PMID: 37289463 PMCID: PMC10251243 DOI: 10.1001/jamaophthalmol.2023.2271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/17/2023] [Indexed: 06/09/2023]
Abstract
Importance Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely. Objective To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information. Design, Setting, and Participants Deidentified color fundus images taken after dilation were used post hoc to train AI systems to perform regression from image to BCVA and to evaluate resultant estimation errors. Participants were patients enrolled in the VISTA randomized clinical trial through 148 weeks wherein the study eye was treated with aflibercept or laser. The data from study participants included macular images, clinical information, and BCVA scores by trained examiners following protocol refraction and VA measurement on Early Treatment Diabetic Retinopathy Study (ETDRS) charts. Main Outcomes Primary outcome was regression evaluated by mean absolute error (MAE); the secondary outcome included percentage of predictions within 10 letters, computed over the entire cohort as well as over subsets categorized by baseline BCVA, determined from baseline through the 148-week visit. Results Analysis included 7185 macular color fundus images of the study and fellow eyes from 459 participants. Overall, the mean (SD) age was 62.2 (9.8) years, and 250 (54.5%) were male. The baseline BCVA score for the study eyes ranged from 73 to 24 letters (approximate Snellen equivalent 20/40 to 20/320). Using ResNet50 architecture, the MAE for the testing set (n = 641 images) was 9.66 (95% CI, 9.05-10.28); 33% of the values (95% CI, 30%-37%) were within 0 to 5 letters and 28% (95% CI, 25%-32%) within 6 to 10 letters. For BCVA of 100 letters or less but more than 80 letters (20/10 to 20/25, n = 161) and 80 letters or less but more than 55 letters (20/32 to 20/80, n = 309), the MAE was 8.84 letters (95% CI, 7.88-9.81) and 7.91 letters (95% CI, 7.28-8.53), respectively. Conclusions and Relevance This investigation suggests AI can estimate BCVA directly from fundus photographs in patients with DME, without refraction or subjective visual acuity measurements, often within 1 to 2 lines on an ETDRS chart, supporting this AI concept if additional improvements in estimates can be achieved.
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Affiliation(s)
- William Paul
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Philippe Burlina
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
- Department of Computer Science and Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, Maryland
- Zoox, Foster City, California
| | - Rohita Mocharla
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Neil Joshi
- Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland
| | - Zhuolin Li
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sophie Gu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York–Presbyterian Hospital, New York, New York
| | - Onnisa Nanegrungsunk
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Kira Lin
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Ruiz Department of Ophthalmology and Visual Science at McGovern Medical School at UTHealth Houston, Houston, Texas
| | - Susan B. Bressler
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Cindy X. Cai
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jun Kong
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - T. Y. Alvin Liu
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Hadi Moini
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Weiming Du
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Fouad Amer
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Karen Chu
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | - Robert Vitti
- Regeneron Pharmaceuticals Inc, Tarrytown, New York
| | | | - Neil M. Bressler
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Editor, JAMA Ophthalmology
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Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee HK, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosens Bioelectron 2023; 229:115233. [PMID: 36965381 DOI: 10.1016/j.bios.2023.115233] [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: 11/07/2022] [Revised: 02/23/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Abstract
Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.
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Affiliation(s)
- Yizhuo Yang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Fang Xu
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Jisen Chen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Chunxu Tao
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Yunxin Li
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China
| | - Quansheng Chen
- College of Ocean Food and Biological Engineering, Jimei University, Xiamen, 361021, Fujian Province, China
| | - Sheng Tang
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
| | - Hian Kee Lee
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China; Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Singapore.
| | - Wei Shen
- School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu Province, China.
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6
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Chagas TA, Dos Reis MA, Leivas G, Santos LP, Gossenheimer AN, Melo GB, Malerbi FK, Schaan BD. Prevalence of diabetic retinopathy in Brazil: a systematic review with meta-analysis. Diabetol Metab Syndr 2023; 15:34. [PMID: 36864478 PMCID: PMC9979496 DOI: 10.1186/s13098-023-01003-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/19/2023] [Indexed: 03/04/2023] Open
Abstract
AIMS To evaluate the prevalence of diabetic retinopathy (DR) in Brazilian adults with diabetes mellitus via a systematic review with meta-analysis. METHODS A systematic review using PubMed, EMBASE, and Lilacs was conducted, searching for studies published up to February 2022. Random effect meta-analysis was performed to estimate the DR prevalence. RESULTS We included 72 studies (n = 29,527 individuals). Among individuals with diabetes in Brazil, DR prevalence was 36.28% (95% CI 32.66-39.97, I2 98%). Diabetic retinopathy prevalence was highest in patients with longer duration of diabetes and in patients from Southern Brazil. CONCLUSION This review shows a similar prevalence of DR as compared to other low- and middle-income countries. However, the high heterogeneity observed-expected in systematic reviews of prevalence-raises concerns about the interpretation of these results, suggesting the need for multicenter studies with representative samples and standardized methodology.
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Affiliation(s)
| | - Mateus Augusto Dos Reis
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
| | - Gabriel Leivas
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Lucas Porto Santos
- National Institute of Science and Technology for Health Technology Assessment (IATS), Porto Alegre, RS, Brazil
| | - Agnes Nogueira Gossenheimer
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Gustavo Barreto Melo
- Department of Ophthalmology and Visual Science, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology and Visual Science, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Beatriz D Schaan
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- National Institute of Science and Technology for Health Technology Assessment (IATS), Porto Alegre, RS, Brazil
- Endocrine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
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7
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Malerbi FK. Artificial intelligence for diabetic retinopathy screening: beyond diagnostic accuracy. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1080. [PMID: 36388776 PMCID: PMC9652528 DOI: 10.21037/atm-22-4756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 10/18/2022] [Indexed: 09/08/2024]
Affiliation(s)
- Fernando Korn Malerbi
- Department of Ophthalmology and Visual Sciences, São Paulo Federal University, São Paulo, Brazil
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8
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Favaloro EJ, Pasalic L, Lippi G. Getting smart with coagulation. J Thromb Haemost 2022; 20:1519-1522. [PMID: 35297174 DOI: 10.1111/jth.15691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 10/18/2022]
Affiliation(s)
- Emmanuel J Favaloro
- Department of Haematology, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, New South Wales, Australia
- Sydney Centres for Thrombosis and Haemostasis, Westmead, New South Wales, Australia
- Faculty of Science and Health, Charles Sturt University, Wagga Wagga, New South Wales, Australia
| | - Leonardo Pasalic
- Department of Haematology, Institute of Clinical Pathology and Medical Research (ICPMR), NSW Health Pathology, Westmead Hospital, Westmead, New South Wales, Australia
- Sydney Centres for Thrombosis and Haemostasis, Westmead, New South Wales, Australia
- University of Sydney, Westmead, New South Wales, Australia
| | - Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
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Liu J, Hu H, Qiu S, Wang D, Liu J, Du Z, Sun Z. The Prevalence and Risk Factors of Diabetic Retinopathy: Screening and Prophylaxis Project in 6 Provinces of China. Diabetes Metab Syndr Obes 2022; 15:2911-2925. [PMID: 36186939 PMCID: PMC9518998 DOI: 10.2147/dmso.s378500] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/06/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To investigate the prevalence and associated factors of diabetic retinopathy (DR) and advanced DR in Chinese adults with diabetes mellitus (DM). PATIENTS AND METHODS A cross-sectional study was performed on 4831 diabetic patients from 24 hospitals from April 2018 to July 2020. Non-mydriatic fundus of patients were interpreted by an artificial intelligence (AI) system. Fundus photos that were unsuitable for AI interpretation were interpreted by two ophthalmologists trained by one expert ophthalmologist at Beijing Tongren Hospital. Medical history, height, weight, body mass index (BMI), glycosylated hemoglobin (HbA1c), blood pressure, and laboratory examinations were recorded. RESULTS A total of 4831 DM patients were included in this study. The prevalence of DR and advanced DR in the diabetic population was 31.8% and 6.6%, respectively. In multiple logistic regression analysis, male (odds ratio [OR], 1.39), duration of diabetes (OR, 1.05), HbA1c (OR, 1.11), farmer (OR, 1.39), insulin treatment (OR, 1.61), region (northern, OR, 1.78; rural, OR, 6.96), and presence of other diabetic complications (OR: 2.03) were associated with increased odds of DR. The factors associated with increased odds of advanced DR included poor glycemic control (HbA1c >7.0%) (OR, 2.58), insulin treatment (OR, 1.73), longer duration of diabetes (OR, 3.66), rural region (OR, 4.84), and presence of other diabetic complications (OR, 2.36), but overweight (BMI > 25 kg/m2) (OR, 0.61) was associated with reduced odds of advanced DR. CONCLUSION This study shows that the prevalence of DR is very high in Chinese adults with DM, highlighting the necessity of early diabetic retinal screening.
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Affiliation(s)
- Jiang Liu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
- Department of Endocrinology, The Third Hospital of Nanchang, Nanchang, Jiangxi, People’s Republic of China
| | - Hao Hu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
- Department of Endocrinology, The First People’s Hospital of Xuzhou, Xuzhou, Jiangsu, People’s Republic of China
| | - Shanhu Qiu
- Department of General Practice, Zhongda Hospital; Institute of Diabetes, School of Medicine, Southeast University, Nanjing, People’s Republic of China
| | - Duolao Wang
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
| | - Jianing Liu
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
| | - Ziwei Du
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
| | - Zilin Sun
- Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China
- Correspondence: Zilin Sun, Department of Endocrinology, Zhongda Hospital, Institute of Diabetes, School of Medicine, Southeast University, Nanjing, 210009, People’s Republic of China, Tel +8613951749490, Fax +862583262609, Email
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