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AlShamlan FT, Bubshait LK, AlAhmad EA, AlOtaibi BS, AlShakhs AA, AlHammad FA. Myopia progression in school children with prolonged screen time during the coronavirus disease confinement. MEDICAL HYPOTHESIS, DISCOVERY & INNOVATION OPHTHALMOLOGY JOURNAL 2023; 12:90-97. [PMID: 38357611 PMCID: PMC10862027 DOI: 10.51329/mehdiophthal1474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/24/2023] [Indexed: 02/16/2024]
Abstract
Background Myopia, the most common refractive error, is a global public health problem with substantial visual impairment if left untreated. Several studies have investigated the association between increased near-work and restricted outdoor activities in children with myopia; however, such studies in children without myopia are scarce. We aimed to monitor the effect of the coronavirus disease-2019 (COVID-19) home confinement and mandatory virtual learning on myopic progression among myopic and non-myopic school-aged children. Methods We conducted a retrospective chart review of children aged 6 - 12 years attending regular visits to the pediatric ophthalmology clinic in a tertiary eye hospital in Eastern Province, Saudi Arabia. Cycloplegic refraction was determined from three visits at least six months apart: two visits before the start of the COVID-19 pandemic and one during the COVID-19 home confinement. Parents were asked about the time spent in near-work and outdoor activities, the devices used during virtual learning, and the demographic characteristics of the children. Statistical analyses were conducted to compare myopia progression before and during the COVID-19 home confinement. Results A total of 160 eyes of 80 children were analyzed. The boy (n = 46) to girl (n = 34) ratio was 1.4:1. The hyperopia (n = 131 eyes) to myopia (n = 29 eyes) ratio was 4.5:1. Most eyes exhibited a hyperopic shift before the confinement; however, all eyes displayed a myopic shift during the confinement. When comparing both eyes of the same individual, the more myopic or less hyperopic eye in the same child had a significantly greater myopic shift than the fellow eye (both P < 0.05). Children who used tablets showed a significant myopic shift (P < 0.05). Likewise, children in both age categories ( ≤ 8 and > 8 years), boys, those living in an apartment, and those having parents with bachelor's degrees experienced a significant myopic shift during COVID-19 home confinement compared to before (all P < 0.05). The mean myopic shift was greater in children aged > 8 years than in those aged ≤ 8 years. Children with and without a family history of myopia had a myopic shift in the mean spherical equivalent during COVID-19 home confinement; however, that of children with no family history was statistically significant (P < 0.05). Conclusions Progression of myopia accelerated in children during the COVID-19 pandemic. Excessive time spent on digital screen devices at near distances is considered a substantial environmental contributor to myopic shift in children. Further multicenter studies with extended follow-up periods are needed to assess the factors contributing to myopic progression in our population.
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Affiliation(s)
- Fatemah T AlShamlan
- Pediatric Ophthalmology and Strabismus Department, Dhahran Eye Specialist Hospital, Dhahran, Saudi Arabia
| | - Luluah K Bubshait
- Department of Ophthalmology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ebtesamah A AlAhmad
- Ophthalmology Department, Dhahran Eye Specialist Hospital, Dhahran, Saudi Arabia
| | - Batool S AlOtaibi
- Ophthalmology Department, Dhahran Eye Specialist Hospital, Dhahran, Saudi Arabia
| | - Abdullah A AlShakhs
- Ophthalmology Department, Dhahran Eye Specialist Hospital, Dhahran, Saudi Arabia
| | - Fatimah A AlHammad
- Ophthalmology Department, Dhahran Eye Specialist Hospital, Dhahran, Saudi Arabia
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Qi J, Pan W, Peng T, Zeng L, Li X, Chen Z, Yang Z, Xu H. Higher Circulating Levels of Neutrophils and Basophils Are Linked to Myopic Retinopathy. Int J Mol Sci 2022; 24:ijms24010080. [PMID: 36613520 PMCID: PMC9820571 DOI: 10.3390/ijms24010080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/08/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
This retrospective study investigated circulating immune cell alteration in patients with myopic retinopathy. Blood test results and demographic and ocular information of 392 myopic patients and 129 emmetropia controls who attended Changsha Aier Eye Hospital from May 2017 to April 2022 were used in this study. Compared with emmetropia, the percentages of neutrophils and basophils and neutrophil/lymphocyte ratio were significantly higher in myopic patients, whereas the percentages of monocytes and lymphocytes and the counts of lymphocytes and eosinophils were significantly lower in myopic patients. After adjusting for age and hypertension/diabetes, the difference remained. Interestingly, the platelet counts were significantly lower in myopic patients after the adjustments. Further subgroup analysis using multivariable linear regression showed that higher levels of neutrophils, neutrophil/lymphocyte ratio, and platelet/lymphocyte ratio, lower levels of monocytes, eosinophils, lymphocytes, and platelets, were related to myopic peripheral retinal degeneration (mPRD) and posterior staphyloma (PS). A higher level of basophils was linked to myopic choroidal neovascularization (mCNV). Our results suggest that higher levels of circulating neutrophils and neutrophil/lymphocyte ratio, lower monocytes, eosinophils, lymphocytes, and platelets are related to mild myopic retinopathy. A higher level of circulating basophils is related to the severe form of myopic retinopathy, such as mCNV.
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Affiliation(s)
- Jinyan Qi
- Aier School of Ophthalmology, Central South University, Changsha 410000, China
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Hunan Province Optometry Engineering and Technology Research Center, Changsha 410000, China
- Hunan Province International Cooperation Base for Optometry Science and Technology, Changsha 410000, China
| | - Wei Pan
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Hunan Province Optometry Engineering and Technology Research Center, Changsha 410000, China
- Hunan Province International Cooperation Base for Optometry Science and Technology, Changsha 410000, China
| | - Ting Peng
- Health Management Center, The Third Xiangya Hospital, Central South University, Changsha 410000, China
| | - Ling Zeng
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Changsha Aier Eye Hospital, Changsha 410000, China
| | - Xiaoning Li
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Hunan Province Optometry Engineering and Technology Research Center, Changsha 410000, China
- Hunan Province International Cooperation Base for Optometry Science and Technology, Changsha 410000, China
- Changsha Aier Eye Hospital, Changsha 410000, China
| | - Zhongping Chen
- Aier School of Ophthalmology, Central South University, Changsha 410000, China
- Changsha Aier Eye Hospital, Changsha 410000, China
| | - Zhikuan Yang
- Aier School of Ophthalmology, Central South University, Changsha 410000, China
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Hunan Province Optometry Engineering and Technology Research Center, Changsha 410000, China
- Hunan Province International Cooperation Base for Optometry Science and Technology, Changsha 410000, China
- Aier School of Optometry and Vision Science, Hubei University of Science and Technology, Xianning 437100, China
| | - Heping Xu
- Aier School of Ophthalmology, Central South University, Changsha 410000, China
- Aier Institute of Optometry and Vision Science, Changsha 410000, China
- Hunan Province Optometry Engineering and Technology Research Center, Changsha 410000, China
- Hunan Province International Cooperation Base for Optometry Science and Technology, Changsha 410000, China
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast BT9 7BL, UK
- Correspondence: ; Tel.: +44-289097-6463
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Du R, Ohno-Matsui K. Novel Uses and Challenges of Artificial Intelligence in Diagnosing and Managing Eyes with High Myopia and Pathologic Myopia. Diagnostics (Basel) 2022; 12:diagnostics12051210. [PMID: 35626365 PMCID: PMC9141019 DOI: 10.3390/diagnostics12051210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 02/04/2023] Open
Abstract
Myopia is a global health issue, and the prevalence of high myopia has increased significantly in the past five to six decades. The high incidence of myopia and its vision-threatening course emphasize the need for automated methods to screen for high myopia and its serious form, named pathologic myopia (PM). Artificial intelligence (AI)-based applications have been extensively applied in medicine, and these applications have focused on analyzing ophthalmic images to diagnose the disease and to determine prognosis from these images. However, unlike diseases that mainly show pathologic changes in the fundus, high myopia and PM generate even more data because both the ophthalmic information and morphological changes in the retina and choroid need to be analyzed. In this review, we present how AI techniques have been used to diagnose and manage high myopia, PM, and other ocular diseases and discuss the current capacity of AI in assisting in preventing high myopia.
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Du R, Xie S, Fang Y, Hagino S, Yamamoto S, Moriyama M, Yoshida T, Igarashi-Yokoi T, Takahashi H, Nagaoka N, Uramoto K, Onishi Y, Watanabe T, Nakao N, Takahashi T, Kaneko Y, Azuma T, Hatake R, Nomura T, Sakura T, Yana M, Xiong J, Chen C, Ohno-Matsui K. Validation of Soft Labels in Developing Deep Learning Algorithms for Detecting Lesions of Myopic Maculopathy From Optical Coherence Tomographic Images. Asia Pac J Ophthalmol (Phila) 2022; 11:227-236. [PMID: 34937047 DOI: 10.1097/apo.0000000000000466] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE It is common for physicians to be uncertain when examining some images. Models trained with human uncertainty could be a help for physicians in diagnosing pathologic myopia. DESIGN This is a hospital-based study that included 9176 images from 1327 patients that were collected between October 2015 and March 2019. METHODS All collected images were graded by 21 myopia specialists according to the presence of myopic neovascularization (MNV), myopic traction maculopathy (MTM), and dome-shaped macula (DSM). Hard labels were made by the rule of major wins, while soft labels were possibilities calculated by whole grading results from the different graders. The area under the curve (AUC) of the receiver operating characteristics curve, the area under precision-recall (AUPR) curve, F-score, and least square errors were used to evaluate the performance of the models. RESULTS The AUC values of models trained by soft labels in MNV, MTM, and DSM models were 0.985, 0.946, and 0.978; and the AUPR values were 0.908, 0.876, and 0.653 respectively. However, 0.56% of MNV "negative" cases were answered as "positive" with high certainty by the hard label model, whereas no case was graded with extreme errors by the soft label model. The same results were found for the MTM (0.95% vs none) and DSM (0.43% vs 0.09%) models. CONCLUSIONS The predicted possibilities from the models trained by soft labels were close to the results made by myopia specialists. These findings could inspire the novel use of deep learning models in the medical field.
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Affiliation(s)
- Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shiqi Xie
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuxin Fang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
- Beijing Tongren Eye Center, Beijing Key Laboratory of Ophthalmology and Visual Science, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | | | | | - Muka Moriyama
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takeshi Yoshida
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tae Igarashi-Yokoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Hiroyuki Takahashi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Natsuko Nagaoka
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kengo Uramoto
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuka Onishi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takashi Watanabe
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Noriko Nakao
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tomonari Takahashi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuichiro Kaneko
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takeshi Azuma
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryoma Hatake
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takuhei Nomura
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tatsuro Sakura
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mariko Yana
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Jianping Xiong
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Changyu Chen
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
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Abstract
INTRODUCTION Vision problems affect academic performance, social and mental health. Most traditional vision screening methods rely on human expert assessments based on a set of vision tests. As technology advances, new instruments and computerised tools are available for complementing vision screening. The scoping review based on this protocol aims to investigate current technologies for vision screening, what vision tests can be complemented by technologies, and how these can support vision screening by providing measurements. METHODS AND ANALYSIS The planned review will utilise the PRISMA extension for Scoping Reviews (PRISMA-ScR) tool. Electronic search will be performed in databases, including Web of Science, MEDLINE (Ovid), Scopus, Engineering Village, Cochrane and Embase. We will perform a systematic search in selected reference databases without the limitation on publications dates, or country of studies. Reference management software, like EndNote and DistillerSR, will be used to remove duplicate entries. Two authors will independently analyse the studies for inclusion eligibility. Conflicts will be resolved by discussion. We will extract the types of technologies, types of vision tests they complement and the measurements for the included studies. Overall findings will be synthesised by thematic analysis and mapping to the logic model. ETHICS AND DISSEMINATION Ethical approval is not required for this review, as it will only summarise existing published data. We will publish the findings in an open access, peer-reviewed journal. We expect that the review results will be useful for vision screening experts, developers, researchers, and policymakers.
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Affiliation(s)
- Qasim Ali
- Department of Computer science, Electrical engineering and Mathematical sciences, Western Norway University of Applied Sciences, Bergen, Hordaland, Norway
| | - Ilona Heldal
- Department of Computer science, Electrical engineering and Mathematical sciences, Western Norway University of Applied Sciences, Bergen, Hordaland, Norway
| | - Carsten Gunnar Helgesen
- Department of Computer science, Electrical engineering and Mathematical sciences, Western Norway University of Applied Sciences, Bergen, Hordaland, Norway
| | - Gunta Krumina
- Department of Optometry and Vision Science, University of Latvia, Riga, Latvia
| | - Marianne Nesbjørg Tvedt
- Department of Computer science, Electrical engineering and Mathematical sciences, Western Norway University of Applied Sciences, Bergen, Hordaland, Norway
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Du R, Xie S, Fang Y, Igarashi-Yokoi T, Moriyama M, Ogata S, Tsunoda T, Kamatani T, Yamamoto S, Cheng CY, Saw SM, Ting D, Wong TY, Ohno-Matsui K. Deep Learning Approach for Automated Detection of Myopic Maculopathy and Pathologic Myopia in Fundus Images. Ophthalmol Retina 2021; 5:1235-1244. [PMID: 33610832 DOI: 10.1016/j.oret.2021.02.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE To determine whether eyes with pathologic myopia can be identified and whether each type of myopic maculopathy lesion on fundus photographs can be diagnosed by deep learning (DL) algorithms. DESIGN A DL algorithm was developed to recognize myopic maculopathy features and to categorize the myopic maculopathy automatically. PARTICIPANTS We examined 7020 fundus images from 4432 highly myopic eyes obtained from the Advanced Clinical Center for Myopia. METHODS Deep learning (DL) algorithms were developed to recognize the key features of myopic maculopathy with 5176 fundus images. These algorithms were also used to develop a Meta-analysis for Pathologic Myopia (META-PM) study categorizing system (CS) by adding a specific processing layer. Models and the system were evaluated by 1844 fundus image. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to determine the performance of each DL algorithm. The rate of correct predictions was used to determine the performance of the META-PM study CS. MAIN OUTCOME MEASURES Four trained DL models were able to recognize the lesions of myopic maculopathy accurately with high sensitivity and specificity. The META-PM study CS also showed a high accuracy and was qualified to be used in a semiautomated way during screening for myopic maculopathy in highly myopic eyes. RESULTS The sensitivity of the DL models was 84.44% for diffuse atrophy, 87.22% for patchy atrophy, 85.10% for macular atrophy, and 37.07% for choroidal neovascularization, and the AUC values were 0.970, 0.978, 0.982, and 0.881, respectively. The rate of total correct predictions from the META-PM study CS was 87.53%, with rates of 90.18%, 95.28%, 97.50%, and 91.14%, respectively, for each type of lesion. The META-PM study CS showed an overall rate of 92.08% in detecting pathologic myopia correctly, which was defined as having myopic maculopathy equal to or more serious than diffuse atrophy. CONCLUSIONS The novel DL models and system can achieve high sensitivity and specificity in identifying the different types of lesions of myopic maculopathy. These results will assist in the screening for pathologic myopia and subsequent protection of patients against low vision and blindness caused by myopic maculopathy.
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Affiliation(s)
- Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shiqi Xie
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuxin Fang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tae Igarashi-Yokoi
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Muka Moriyama
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Satoko Ogata
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan; Department of Medical Science Mathematics, Tokyo Medical and Dental University, Tokyo, Japan; Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takashi Kamatani
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan; Department of Medical Science Mathematics, Tokyo Medical and Dental University, Tokyo, Japan; Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | | | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Seang-Mei Saw
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Daniel Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Tien Y Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Republic of Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, Tokyo, Japan.
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Modjtahedi BS, Abbott RL, Fong DS, Lum F, Tan D. Reducing the Global Burden of Myopia by Delaying the Onset of Myopia and Reducing Myopic Progression in Children: The Academy's Task Force on Myopia. Ophthalmology 2020; 128:816-826. [PMID: 33388160 DOI: 10.1016/j.ophtha.2020.10.040] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 01/08/2023] Open
Abstract
In 2019, the American Academy of Ophthalmology (AAO) created the Task Force on Myopia in recognition of the substantial global increases in myopia prevalence and its associated complications. The Task Force, led by Richard L. Abbott, MD, and Donald Tan, MD, comprised recognized experts in myopia prevention and treatment, public health experts from around the world, and organization representatives from the American Academy of Family Physicians, American Academy of Optometry, and American Academy of Pediatrics. The Academy's Board of Trustees believes that myopia is a high-priority cause of visual impairment, warranting a timely evaluation and synthesis of the scientific literature and formulation of an action plan to address the issue from different perspectives. This includes education of physicians and other health care providers, patients and their families, schools, and local and national public health agencies; defining health policies to ameliorate patients' access to appropriate therapy and to promote effective public health interventions; and fostering promising avenues of research.
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Affiliation(s)
- Bobeck S Modjtahedi
- Department of Ophthalmology, Southern California Kaiser Permanente, Baldwin Park, California; Department of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California
| | - Richard L Abbott
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Donald S Fong
- Department of Ophthalmology, Southern California Kaiser Permanente, Baldwin Park, California; Department of Research and Evaluation, Southern California Permanente Medical Group, Pasadena, California
| | - Flora Lum
- American Academy of Ophthalmology, San Francisco, California.
| | - Donald Tan
- Eye and Retina Surgeons, Singapore, Republic of Singapore
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Recent Epidemiology Study Data of Myopia. J Ophthalmol 2020; 2020:4395278. [PMID: 33489329 PMCID: PMC7803099 DOI: 10.1155/2020/4395278] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/21/2020] [Indexed: 12/22/2022] Open
Abstract
Myopia, a pandemic refractive error, is affecting more and more people. The progression of myopia could cause numerously serious complications, even leading to blindness. This review summarizes the epidemiological studies on myopia after 2018 and analyzes the risk factors associated with myopia. The prevalence of myopia varies in different regions, age, and observation time. East Asia has been gripped by an unprecedented rise in myopia, and other parts of the world have also seen an increase. The prevalence of myopia in children continues to rise and aggravates with age. The prevalence of high myopia has also increased along with myopia. Racial dependence and family aggregation can be seen frequently in myopia patients. Increased outdoor activities are proven to be protective factors for myopia, as near-distance work and higher education levels affect in the opposite. The impact of gender or urbanization on myopia is controversial. The relationship between nutrition, digital screens, Kawasaki disease, pregnant women smoking during pregnancy, and myopia is still not clear for lack of sufficient evidence. Understanding the various factors that affect myopia helps to clarify the mechanism of myopia formation and also to formulate reasonable prevention and control measures of myopia to protect people's health, especially for adolescents.
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