{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T01:07:22Z","timestamp":1791594442548,"version":"4.3.4"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010167","name":"Shanghai Shen Kang Hospital Development Center","doi-asserted-by":"publisher","award":["SHDC2020CR30538"],"award-info":[{"award-number":["SHDC2020CR30538"]}],"id":[{"id":"10.13039\/501100010167","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC2702100"],"award-info":[{"award-number":["2021YFC2702100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:p>Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.<\/jats:p>","DOI":"10.3389\/fcell.2023.1124005","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T05:31:44Z","timestamp":1673933504000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Artificial intelligence technology for myopia challenges: A review"],"prefix":"10.3389","volume":"11","author":[{"given":"Juzhao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haidong","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/ICEngTechnol.2017.8308186","article-title":"Understanding of a convolutional neural network","author":"Albawi","year":"2017"},{"key":"B2","doi-asserted-by":"publisher","first-page":"264","DOI":"10.22608\/APO.2018479","article-title":"Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology","volume":"8","author":"Balyen","year":"2019","journal-title":"Asia Pac J. Ophthalmol. (Phila)."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.1136\/bmj.332.7549.1089","article-title":"Comparative accuracy: Assessing new tests against existing diagnostic pathways","volume":"332","author":"Bossuyt","year":"2006","journal-title":"Bmj"},{"key":"B4","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1001\/jamaophthalmol.2018.1934","article-title":"Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks","volume":"136","author":"Brown","year":"2018","journal-title":"JAMA Ophthalmol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.1109\/EMBC44109.2020.9176184","article-title":"Volumetric choroidal segmentation using sequential deep learning approach in high myopia subjects","volume":"2020","author":"Cahyo","year":"2020","journal-title":"Annu. Int. Conf. IEEE Eng. Med. Biol. Soc."},{"key":"B6","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1016\/j.ajo.2013.01.016","article-title":"Myopia-related fundus changes in Singapore adults with high myopia","volume":"155","author":"Chang","year":"2013","journal-title":"Am. J. Ophthalmol."},{"key":"B7","doi-asserted-by":"crossref","DOI":"10.1109\/GUCON.2018.8675097","article-title":"A review on conventional machine learning vs deep learning","author":"Chauhan","year":"2018"},{"key":"B8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1167\/tvst.11.2.38","article-title":"Application of artificial intelligence and deep learning for choroid segmentation in myopia","volume":"11","author":"Chen","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1016\/j.ophtha.2019.06.026","article-title":"Contribution of genome-wide significant single nucleotide polymorphisms in myopia prediction: Findings from a 10-year cohort of Chinese twin children","volume":"126","author":"Chen","year":"2019","journal-title":"Ophthalmology"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.18240\/ijo.2021.09.01","article-title":"Who needs myopia control?","volume":"14","author":"Chen","year":"2021","journal-title":"Int. J. Ophthalmol."},{"key":"B11","doi-asserted-by":"publisher","first-page":"21663","DOI":"10.1038\/s41598-021-00622-x","article-title":"Deep learning models for screening of high myopia using optical coherence tomography","volume":"11","author":"Choi","year":"2021","journal-title":"Sci. Rep."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat. Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.ajo.2019.10.015","article-title":"Applying machine learning techniques in nomogram prediction and analysis for SMILE treatment","volume":"210","author":"Cui","year":"2020","journal-title":"Am. J. Ophthalmol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B15","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s13073-019-0689-8","article-title":"Artificial intelligence in clinical and genomic diagnostics","volume":"11","author":"Dias","year":"2019","journal-title":"Genome Med."},{"key":"B16","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1097\/IAE.0000000000002590","article-title":"Prevalence and time trends of myopia in children and adolescents in China: A systemic review and meta-analysis","volume":"40","author":"Dong","year":"2020","journal-title":"Retina"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.3390\/diagnostics12051210","article-title":"Novel uses and challenges of artificial intelligence in diagnosing and managing eyes with high myopia and pathologic myopia","volume":"12","author":"Du","year":"2022","journal-title":"Diagn. (Basel)"},{"key":"B18","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1097\/APO.0000000000000466","article-title":"Validation of soft labels in developing deep learning algorithms for detecting lesions of myopic maculopathy from optical coherence tomographic images","volume":"11","author":"Du","year":"2022","journal-title":"Asia Pac J. Ophthalmol. (Phila)."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1016\/j.oret.2021.02.006","article-title":"Deep learning approach for automated detection of myopic maculopathy and pathologic myopia in fundus images","volume":"5","author":"Du","year":"2021","journal-title":"Ophthalmol. Retina"},{"key":"B20","doi-asserted-by":"publisher","first-page":"459","DOI":"10.3760\/cma.j.cn112142-20201121-00767","article-title":"Research advances of morphological changes of the choroid in high myopia","volume":"57","author":"Duan","year":"2021","journal-title":"Zhonghua Yan Ke Za Zhi"},{"key":"B21","doi-asserted-by":"crossref","first-page":"34","DOI":"10.47102\/annals-acadmedsg.V33N1p34","article-title":"The epidemiology of myopia in Hong Kong","volume":"33","author":"Edwards","year":"2004","journal-title":"Ann. Acad. Med. Singap"},{"key":"B22","doi-asserted-by":"publisher","first-page":"101330","DOI":"10.1016\/j.clae.2020.05.001","article-title":"Machine learning based strategy surpasses the traditional method for selecting the first trial Lens parameters for corneal refractive therapy in Chinese adolescents with myopia","volume":"45","author":"Fan","year":"2022","journal-title":"Cont. Lens Anterior Eye"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1053079","DOI":"10.3389\/fcell.2022.1053079","article-title":"Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program","volume":"10","author":"Fang","year":"2022","journal-title":"Front. Cell Dev. Biol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1097\/ICU.0000000000000791","article-title":"Artificial intelligence in myopia: Current and future trends","volume":"32","author":"Foo","year":"2021","journal-title":"Curr. Opin. Ophthalmol."},{"key":"B25","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1111\/opo.12579","article-title":"A genetic risk score and number of myopic parents independently predict myopia","volume":"38","author":"Ghorbani Mojarrad","year":"2018","journal-title":"Ophthalmic Physiol. Opt."},{"key":"B26","doi-asserted-by":"publisher","first-page":"M184","DOI":"10.1167\/iovs.18-25977","article-title":"Imi - clinical management guidelines report","volume":"60","author":"Gifford","year":"2019","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"B27","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"Jama"},{"key":"B28","doi-asserted-by":"publisher","first-page":"411","DOI":"10.4103\/ijmr.IJMR_1369_20","article-title":"COVID-19 pandemic from an ophthalmology point of view","volume":"151","author":"Gupta","year":"2020","journal-title":"Indian J. Med. Res."},{"key":"B29","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s40662-021-00273-z","article-title":"Application of artificial intelligence in cataract management: Current and future directions","volume":"9","author":"Gutierrez","year":"2022","journal-title":"Eye Vis. (Lond)."},{"key":"B30","doi-asserted-by":"publisher","first-page":"e0211204","DOI":"10.1371\/journal.pone.0211204","article-title":"Prevalence and risk factors of myopia in adult Korean population: Korea national health and nutrition examination survey 2013-2014 (KNHANES VI)","volume":"14","author":"Han","year":"2019","journal-title":"PLoS One"},{"key":"B31","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1038\/s41433-021-01805-6","article-title":"Myopia prediction: A systematic review","volume":"36","author":"Han","year":"2022","journal-title":"Eye (Lond)."},{"key":"B32","doi-asserted-by":"publisher","first-page":"105920","DOI":"10.1016\/j.cmpb.2020.105920","article-title":"Pathological myopia classification with simultaneous lesion segmentation using deep learning","volume":"199","author":"Hemelings","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"B33","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1016\/j.ophtha.2016.01.006","article-title":"Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050","volume":"123","author":"Holden","year":"2016","journal-title":"Ophthalmology"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1580","DOI":"10.1364\/BOE.387807","article-title":"Comparative study of deep learning models for optical coherence tomography angiography","volume":"11","author":"Jiang","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"B35","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1038\/eye.2013.223","article-title":"Histological changes of high axial myopia","volume":"28","author":"Jonas","year":"2014","journal-title":"Eye (Lond)."},{"key":"B36","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s15010-017-1023-2","article-title":"Infectious keratitis and orthokeratology lens use: A systematic review","volume":"45","author":"Kam","year":"2017","journal-title":"Infection"},{"key":"B37","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.ajo.2021.02.006","article-title":"Prediction of phakic intraocular lens vault using machine learning of anterior segment optical coherence tomography metrics","volume":"226","author":"Kamiya","year":"2021","journal-title":"Am. J. Ophthalmol."},{"key":"B38","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1167\/tvst.10.6.5","article-title":"Development of a web-based ensemble machine learning application to select the optimal size of posterior chamber phakic intraocular lens","volume":"10","author":"Kang","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B39","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1001\/jamaophthalmol.2018.6035","article-title":"Visualizing deep learning models for the detection of referable diabetic retinopathy and glaucoma","volume":"137","author":"Keel","year":"2019","journal-title":"JAMA Ophthalmol."},{"key":"B40","doi-asserted-by":"publisher","first-page":"100951","DOI":"10.1016\/j.preteyeres.2021.100951","article-title":"Retinal applications of swept source optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA)","volume":"84","author":"La\u00edns","year":"2021","journal-title":"Prog. Retin Eye Res."},{"key":"B41","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s40662-022-00285-3","article-title":"Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks","volume":"9","author":"Li","year":"2022","journal-title":"Eye Vis. (Lond)."},{"key":"B42","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1167\/tvst.10.13.12","article-title":"Automated analysis of choroidal sublayer morphologic features in myopic children using EDI-OCT by deep learning","volume":"10","author":"Li","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s40123-021-00450-2","article-title":"Machine learning to determine risk factors for myopia progression in primary school children: The anyang childhood eye study","volume":"11","author":"Li","year":"2022","journal-title":"Ophthalmol. Ther."},{"key":"B44","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"B45","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1136\/bjophthalmol-2020-317825","article-title":"Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images","volume":"106","author":"Li","year":"2022","journal-title":"Br. J. Ophthalmol."},{"key":"B46","doi-asserted-by":"publisher","first-page":"813856","DOI":"10.3389\/fped.2021.813856","article-title":"Prevalence of visual impairment among students before and during the COVID-19 pandemic, findings from 1, 057, 061 individuals in guangzhou, southern China","volume":"9","author":"Liang","year":"2021","journal-title":"Front. Pediatr."},{"key":"B47","doi-asserted-by":"publisher","first-page":"e1002674","DOI":"10.1371\/journal.pmed.1002674","article-title":"Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study","volume":"15","author":"Lin","year":"2018","journal-title":"PLoS Med."},{"key":"B48","doi-asserted-by":"publisher","first-page":"e40249","DOI":"10.2196\/40249","article-title":"Medical staff and resident preferences for using deep learning in eye disease screening: Discrete choice experiment","volume":"24","author":"Lin","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"B49","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.106","article-title":"Feature Pyramid networks for object detection","author":"Lin","year":"2017"},{"key":"B50","doi-asserted-by":"publisher","first-page":"719262","DOI":"10.3389\/fcell.2021.719262","article-title":"AI-model for identifying pathologic myopia based on deep learning algorithms of myopic maculopathy classification and \"plus\" lesion detection in fundus images","volume":"9","author":"Lu","year":"2021","journal-title":"Front. Cell Dev. Biol."},{"key":"B51","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1016\/S0140-6736(12)60272-4","volume":"379","author":"Morgan","year":"2012","journal-title":"Myopia. Lancet."},{"key":"B52","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1001\/jamaophthalmol.2019.2923","article-title":"Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone","volume":"137","author":"Natarajan","year":"2019","journal-title":"JAMA Ophthalmol."},{"key":"B53","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.preteyeres.2009.12.002","article-title":"The multifunctional choroid","volume":"29","author":"Nickla","year":"2010","journal-title":"Prog. Retin Eye Res."},{"key":"B54","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future - big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"B55","doi-asserted-by":"publisher","first-page":"742","DOI":"10.3390\/diagnostics12030742","article-title":"Deep learning model based on 3D optical coherence tomography images for the automated detection of pathologic myopia","volume":"12","author":"Park","year":"2022","journal-title":"Diagn. (Basel)."},{"key":"B56","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.ophtha.2018.11.015","article-title":"DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs","volume":"126","author":"Peng","year":"2019","journal-title":"Ophthalmology"},{"key":"B57","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1111\/cxo.12862","article-title":"Choroidal changes in human myopia: Insights from optical coherence tomography imaging","volume":"102","author":"Read","year":"2019","journal-title":"Clin. Exp. Optom."},{"key":"B58","volume-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"B59","doi-asserted-by":"crossref","DOI":"10.1109\/FTC.2016.7821697","article-title":"Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data","author":"Sarraf","year":"2016"},{"key":"B60","doi-asserted-by":"publisher","first-page":"15808","DOI":"10.1038\/s41598-021-94178-5","article-title":"Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia","volume":"11","author":"Scheetz","year":"2021","journal-title":"Sci. Rep."},{"key":"B61","doi-asserted-by":"publisher","first-page":"e133","DOI":"10.3928\/23258160-20190503-13","article-title":"Choriocapillary blood flow in myopic subjects measured with OCT angiography","volume":"50","author":"Scherm","year":"2019","journal-title":"Ophthalmic Surg. Lasers Imaging Retina"},{"key":"B62","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"B63","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1167\/tvst.10.9.23","article-title":"Quantitative assessment of fundus tessellated density and associated factors in fundus images using artificial intelligence","volume":"10","author":"Shao","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B64","doi-asserted-by":"publisher","first-page":"319618","DOI":"10.1136\/bjophthalmol-2021-319618","article-title":"Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction","volume":"2021","author":"Shen","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"B65","doi-asserted-by":"publisher","first-page":"e3460","DOI":"10.1002\/cnm.3460","article-title":"A method for the automatic detection of myopia in Optos fundus images based on deep learning","volume":"37","author":"Shi","year":"2021","journal-title":"Int. J. Numer. Method Biomed. Eng."},{"key":"B66","unstructured":"Deep inside convolutional networks: Visualising image classification models and saliency maps SimonyanK. VedaldiA. ZissermanA. 2013"},{"key":"B67","doi-asserted-by":"publisher","first-page":"e0227240","DOI":"10.1371\/journal.pone.0227240","article-title":"Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography","volume":"15","author":"Sogawa","year":"2020","journal-title":"PLoS One"},{"key":"B68","doi-asserted-by":"publisher","first-page":"657566","DOI":"10.3389\/fmed.2021.657566","article-title":"Imaging features by machine learning for quantification of optic disc changes and impact on choroidal thickness in young myopic patients","volume":"8","author":"Sun","year":"2021","journal-title":"Front. Med. (Lausanne)."},{"key":"B69","unstructured":"EfficientNet: Rethinking model scaling for convolutional neural networ ks TanM. LeQ. V. 2019"},{"key":"B70","doi-asserted-by":"publisher","first-page":"e317","DOI":"10.1016\/S2589-7500(21)00055-8","article-title":"Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: A retrospective multicohort study","volume":"3","author":"Tan","year":"2021","journal-title":"Lancet Digit. Health"},{"key":"B71","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s40662-020-00214-2","article-title":"A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children","volume":"7","author":"Tang","year":"2020","journal-title":"Eye Vis. (Lond)."},{"key":"B72","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1038\/s41588-018-0127-7","article-title":"Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error","volume":"50","author":"Tedja","year":"2018","journal-title":"Nat. Genet."},{"key":"B73","doi-asserted-by":"publisher","first-page":"2781","DOI":"10.1167\/iovs.19-26580","article-title":"Trends in the prevalence of myopia and myopic maculopathy in a Japanese population: The hisayama study","volume":"60","author":"Ueda","year":"2019","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"B74","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1167\/iovs.18-23887","article-title":"Deep learning for predicting refractive error from retinal fundus images","volume":"59","author":"Varadarajan","year":"2018","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"B75","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1038\/ng.2554","article-title":"Genome-wide meta-analyses of multiancestry cohorts identify multiple new susceptibility loci for refractive error and myopia","volume":"45","author":"Verhoeven","year":"2013","journal-title":"Nat. Genet."},{"key":"B76","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1109\/TMI.2017.2655486","article-title":"Detecting cardiovascular disease from mammograms with deep learning","volume":"36","author":"Wang","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"B77","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/jcm10010111","article-title":"Evaluating the effect of topical atropine use for myopia control on intraocular pressure by using machine learning","volume":"10","author":"Wu","year":"2020","journal-title":"J. Clin. Med."},{"key":"B78","doi-asserted-by":"publisher","first-page":"463","DOI":"10.3390\/ijerph17020463","article-title":"Prediction of myopia in adolescents through machine learning methods","volume":"17","author":"Yang","year":"2020","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"B79","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1167\/tvst.10.13.10","article-title":"Automatic screening and identifying myopic maculopathy on optical coherence tomography images using deep learning","volume":"10","author":"Ye","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"B80","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1038\/s41433-021-01795-5","article-title":"Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images","volume":"36","author":"Yoo","year":"2022","journal-title":"Eye (Lond)."},{"key":"B81","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","article-title":"Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study","volume":"15","author":"Zech","year":"2018","journal-title":"PLoS Med."},{"key":"B82","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Visualizing and understanding convolutional networks","author":"Zeiler","year":"2014"},{"key":"B83","doi-asserted-by":"publisher","first-page":"840498","DOI":"10.3389\/fmed.2022.840498","article-title":"Applications of artificial intelligence in myopia: Current and future directions","volume":"9","author":"Zhang","year":"2022","journal-title":"Front. Med. (Lausanne)"},{"key":"B84","doi-asserted-by":"publisher","first-page":"9825","DOI":"10.1038\/s41598-017-10660-z","article-title":"The relationship between scleral staphyloma and choroidal thinning in highly myopic eyes: The Beijing Eye Study","volume":"7","author":"Zhou","year":"2017","journal-title":"Sci. Rep."}],"container-title":["Frontiers in Cell and Developmental Biology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2023.1124005\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T21:12:01Z","timestamp":1701724321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2023.1124005\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":84,"alternative-id":["10.3389\/fcell.2023.1124005"],"URL":"https:\/\/doi.org\/10.3389\/fcell.2023.1124005","relation":{},"ISSN":["2296-634X"],"issn-type":[{"value":"2296-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]},"article-number":"1124005"}}