{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:04:54Z","timestamp":1771326294642,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:00:00Z","timestamp":1725753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:00:00Z","timestamp":1725753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Key Research and Development Project of the Ministry of Science and Technology of the People\u2019s Republic of China","award":["2020YFF0305104"],"award-info":[{"award-number":["2020YFF0305104"]}]},{"name":"Key Research and Development Project of the Science & Technology Department of Sichuan Province","award":["2020YFS0324"],"award-info":[{"award-number":["2020YFS0324"]}]},{"name":"the Science and Technology Innovation Program of Hunan Province","award":["2023RC1079"],"award-info":[{"award-number":["2023RC1079"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-024-00989-4","type":"journal-article","created":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T12:01:59Z","timestamp":1725796919000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["AI sees beyond humans: automated diagnosis of myopia based on peripheral refraction map using interpretable deep learning"],"prefix":"10.1186","volume":"11","author":[{"given":"Yong","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghua","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linjing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longbo","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongli","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhikuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weizhong","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,8]]},"reference":[{"issue":"7553","key":"989_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.","journal-title":"Nature"},{"issue":"3","key":"989_CR2","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/JPROC.2021.3060483","volume":"109","author":"W Samek","year":"2021","unstructured":"Samek W, Montavon G, Lapuschkin S, Anders CJ, M\u00fcller KR. Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE. 2021;109(3):247\u201378. https:\/\/doi.org\/10.1109\/JPROC.2021.3060483.","journal-title":"Proc IEEE"},{"issue":"7587","key":"989_CR3","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"28","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ, et al. Mastering the game of go with deep neural networks and tree search. Nat Jan. 2016;28(7587):484\u20139. https:\/\/doi.org\/10.1038\/nature16961.","journal-title":"Nat Jan"},{"issue":"7676","key":"989_CR4","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1038\/nature24270","volume":"18","author":"D Silver","year":"2017","unstructured":"Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nat Oct. 2017;18(7676):354\u20139. https:\/\/doi.org\/10.1038\/nature24270.","journal-title":"Nat Oct"},{"issue":"3","key":"989_CR5","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1148\/radiol.2017170706","volume":"286","author":"K Yasaka","year":"2018","unstructured":"Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with Convolutional Neural Network for differentiation of Liver masses at Dynamic contrast-enhanced CT: a preliminary study. Radiol Mar. 2018;286(3):887\u201396. https:\/\/doi.org\/10.1148\/radiol.2017170706.","journal-title":"Radiol Mar"},{"issue":"4","key":"989_CR6","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449\u201359. https:\/\/doi.org\/10.1007\/s10278-017-9983-4.","journal-title":"J Digit Imaging"},{"issue":"4","key":"989_CR7","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1002\/jmri.26534","volume":"49","author":"MA Mazurowski","year":"2019","unstructured":"Mazurowski MA, Buda M, Saha A, Bashir MR. Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI. J Magn Reson Imaging. 2019;49(4):939\u201354. https:\/\/doi.org\/10.1002\/jmri.26534.","journal-title":"J Magn Reson Imaging"},{"key":"989_CR8","doi-asserted-by":"publisher","unstructured":"Van Sloun RJ, Cohen R, Eldar YC. Deep learning in ultrasound imaging. Proceedings of the IEEE. 2019;108(1):11\u201329. https:\/\/doi.org\/10.1109\/JPROC.2019.2932116","DOI":"10.1109\/JPROC.2019.2932116"},{"issue":"2","key":"989_CR9","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","volume":"103","author":"DSW Ting","year":"2019","unstructured":"Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167\u201375. https:\/\/doi.org\/10.1136\/bjophthalmol-2018-313173.","journal-title":"Br J Ophthalmol"},{"issue":"9","key":"989_CR10","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24(9):1342\u201350.","journal-title":"Nat Med"},{"key":"989_CR11","doi-asserted-by":"publisher","first-page":"100759","DOI":"10.1016\/j.preteyeres.2019.04.003","volume":"72","author":"DS Ting","year":"2019","unstructured":"Ting DS, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Progress Retinal. 2019;72:100759. https:\/\/doi.org\/10.1016\/j.preteyeres.2019.04.003.","journal-title":"Progress Retinal"},{"issue":"5","key":"989_CR12","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172(5):1122\u201331. e9. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010.","journal-title":"Cell"},{"issue":"1","key":"989_CR13","doi-asserted-by":"publisher","first-page":"16203","DOI":"10.1038\/s41598-019-52533-7","volume":"7","author":"W Lan","year":"2019","unstructured":"Lan W, Lin Z, Yang Z, Artal P. Two-dimensional peripheral refraction and retinal image quality in emmetropic children. Sci Rep Nov. 2019;7(1):16203. https:\/\/doi.org\/10.1038\/s41598-019-52533-7.","journal-title":"Sci Rep Nov"},{"key":"989_CR14","doi-asserted-by":"publisher","unstructured":"Wang S, Lin Z, Xi X et al. Two-dimensional, high-resolution peripheral refraction in adults with isomyopia and anisomyopia. Investigative ophthalmology & visual science. 2020;61(6):16\u201316. https:\/\/doi.org\/10.1167\/iovs.61.6.16.","DOI":"10.1167\/iovs.61.6.16"},{"issue":"7","key":"989_CR15","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.1364\/BOE.397077","volume":"11","author":"Z Lin","year":"2020","unstructured":"Lin Z, Duarte-Toledo R, Manzanera S, Lan W, Artal P, Yang Z. Two-dimensional peripheral refraction and retinal image quality in orthokeratology lens wearers. Biomedical Opt Express 2020\/07\/01. 2020;11(7):3523\u201333. https:\/\/doi.org\/10.1364\/BOE.397077.","journal-title":"Biomedical Opt Express 2020\/07\/01"},{"issue":"10","key":"989_CR16","doi-asserted-by":"publisher","first-page":"2206","DOI":"10.1364\/JOSAA.26.002206","volume":"26","author":"FS Juan Tabernero","year":"2009","unstructured":"Juan Tabernero FS. Fast scanning photoretinoscope for measuring peripheral refraction as a function of accommodation. J Opt Soc Am Opt Image Sci Vis. 2009;26(10):2206\u201310.","journal-title":"J Opt Soc Am Opt Image Sci Vis"},{"issue":"4","key":"989_CR17","doi-asserted-by":"publisher","first-page":"e0213574","DOI":"10.1371\/journal.pone.0213574","volume":"14","author":"M Garcia Garcia","year":"2019","unstructured":"Garcia Garcia M, Pusti D, Wahl S, Ohlendorf A. A global approach to describe retinal defocus patterns. PLoS ONE. 2019;14(4):e0213574. https:\/\/doi.org\/10.1371\/journal.pone.0213574.","journal-title":"PLoS ONE"},{"key":"989_CR18","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <\u20090.5\u00a0MB model size. arXiv preprint arXiv:07360. 2016."},{"key":"989_CR19","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar F, Korkmaz D. COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses. 2020;140:109761. https:\/\/doi.org\/10.1016\/j.mehy.2020.109761.","journal-title":"Med Hypotheses"},{"key":"989_CR20","doi-asserted-by":"crossref","unstructured":"Nakamichi K, Lu H, Kim H, Yoneda K, Tanaka F. Classification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on SqueezeNet. 2019.","DOI":"10.23919\/ICCAS47443.2019.8971646"},{"key":"989_CR21","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A. Learning deep features for discriminative localization. 2016:2921\u20139.","DOI":"10.1109\/CVPR.2016.319"},{"key":"989_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: visual explanations from deep networks via gradient-based localization. 2017:618\u201326.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"7553","key":"989_CR23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344.","journal-title":"Nature"},{"issue":"13","key":"989_CR24","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","volume":"319","author":"AL Beam","year":"2018","unstructured":"Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. Jama. 2018;319(13):1317\u20138. https:\/\/doi.org\/10.1001\/jama.2017.18391.","journal-title":"JAMA"},{"key":"989_CR25","doi-asserted-by":"crossref","unstructured":"Dodge S, Karam L. A study and comparison of human and deep learning recognition performance under visual distortions. IEEE; 2017:1\u20137.","DOI":"10.1109\/ICCCN.2017.8038465"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-00989-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-024-00989-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-00989-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T12:05:44Z","timestamp":1725797144000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-024-00989-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,8]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["989"],"URL":"https:\/\/doi.org\/10.1186\/s40537-024-00989-4","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,8]]},"assertion":[{"value":"14 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All experimental protocols met the tenets of the Declaration of Helsinki and had been approved by the Ethical Committee of Aier Eye Hospital Groups (AIER2018IRB15).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"125"}}