{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:09:58Z","timestamp":1792858198332,"version":"4.5.0"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019M3E5D1A02070862"],"award-info":[{"award-number":["2019M3E5D1A02070862"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1G1A1098951"],"award-info":[{"award-number":["2019R1G1A1098951"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Purpose<\/jats:title>\n<jats:p>Considering the scarcity of normal and strabismic images, this study proposed a method that combines a meta-learning approach with image processing methods to improve the classification accuracy when meta-learning alone is used for screening strabismus.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>The meta-learning approach was first pre-trained on a public dataset to obtain a well-generalized embedding network to extract distinctive features of images. On the other hand, the image processing methods were used to extract the position features of eye regions (e.g., iris position, corneal light reflex) as supplementary features to the distinctive features. Afterward, principal component analysis was applied to reduce the dimensionality of distinctive features for integration with low-dimensional supplementary features. The integrated features were then used to train a support vector machine classifier for performing strabismus screening. Sixty images (30 normal and 30 strabismus) were used to verify the effectiveness of the proposed method, and its classification performance was assessed by computing the accuracy, specificity, and sensitivity through 5,000 experiments.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>The proposed method achieved a classification accuracy of 0.805 with a sensitivity (correct classification of strabismus) of 0.768 and a specificity (correct classification of normal) of 0.842, whereas the classification accuracy of using meta-learning alone was 0.709 with a sensitivity of 0.740 and a specificity of 0.678.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The proposed strabismus screening method achieved promising classification accuracy and gained significant accuracy improvement over using meta-learning alone under data scarcity.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0269365","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T17:30:53Z","timestamp":1659720653000},"page":"e0269365","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["An improved strabismus screening method with combination of meta-learning and image processing under data scarcity"],"prefix":"10.1371","volume":"17","author":[{"given":"Xilang","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6673-569X","authenticated-orcid":true,"given":"Sang Joon","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0807-6582","authenticated-orcid":true,"given":"Chang Zoo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-7723","authenticated-orcid":true,"given":"Seon Han","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"issue":"10","key":"pone.0269365.ref001","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.ophtha.2018.04.024","article-title":"Strabismus, strabismus surgery, and reoperation rate in the united states: analysis from the IRIS Registry","volume":"125","author":"MX Repka","year":"2018","journal-title":"Ophthalmology"},{"issue":"6","key":"pone.0269365.ref002","doi-asserted-by":"crossref","first-page":"e0129035","DOI":"10.1371\/journal.pone.0129035","article-title":"Muscle Union Procedure in Patients with Paralytic Strabismus","volume":"10","author":"KA Park","year":"2015","journal-title":"PLOS ONE"},{"issue":"1","key":"pone.0269365.ref003","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1001\/jamaophthalmol.2014.3941","article-title":"Risk of musculoskeletal injuries, fractures, and falls in medicare beneficiaries with disorders of binocular vision","volume":"133","author":"SL Pineles","year":"2015","journal-title":"JAMA Ophthalmol"},{"key":"pone.0269365.ref004","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.ophtha.2008.08.031","article-title":"Fixation preference and visual acuity testing in a population-based cohort of preschool children with amblyopia risk factors","volume":"116","author":"SA Cotter","year":"2009","journal-title":"Ophthalmology"},{"issue":"2","key":"pone.0269365.ref005","doi-asserted-by":"crossref","first-page":"e56556","DOI":"10.1371\/journal.pone.0056556","article-title":"Neurological Basis for Eye Movements of the Blind","volume":"8","author":"RM Schneider","year":"2013","journal-title":"PLOS ONE"},{"issue":"1","key":"pone.0269365.ref006","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.compbiomed.2011.11.001","article-title":"Computational methodology for automatic detection of strabismus in digital images through Hirschberg test","volume":"42","author":"JD Almeida","year":"2012","journal-title":"Comput Biol Med"},{"issue":"4","key":"pone.0269365.ref007","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1007\/s10278-014-9758-0","article-title":"Computer-Aided Methodology for Syndromic Strabismus Diagnosis","volume":"28","author":"JD Sousa de Almeida","year":"2015","journal-title":"J Digit Imaging"},{"issue":"8","key":"pone.0269365.ref008","doi-asserted-by":"crossref","first-page":"e0255643","DOI":"10.1371\/journal.pone.0255643","article-title":"An automatic screening method for strabismus detection based on image processing","volume":"16","author":"XL Huang","year":"2021","journal-title":"PLOS ONE"},{"key":"pone.0269365.ref009","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.cmpb.2017.01.002","article-title":"Automatic diagnosis of strabismus in digital videos through cover test","volume":"140","author":"TLA Valente","year":"2017","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0269365.ref010","unstructured":"Lu J, Fan Z, Zheng C, et al. Automated strabismus detection for telemedicine applications. arXiv:1809.02940, 2018"},{"issue":"1","key":"pone.0269365.ref011","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1167\/tvst.10.1.33","article-title":"Detection of Referable Horizontal Strabismus in Children\u2019s Primary Gaze Photographs Using Deep Learning","volume":"10","author":"C Zheng","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"pone.0269365.ref012","unstructured":"Schmidhuber J. Evolutionary Principles in Self-referential Learning, or on Learning How to Learn: The Meta-meta-\u2026Hook. PhD thesis, Technische Univ. M\u00fcnchen, 1987"},{"key":"pone.0269365.ref013","doi-asserted-by":"crossref","unstructured":"Thrun S, Pratt L. (eds) Learning to learn. Kluwer Academic Publishers, Norwell, MA, USA, 1998.","DOI":"10.1007\/978-1-4615-5529-2"},{"key":"pone.0269365.ref014","doi-asserted-by":"crossref","unstructured":"Mahajan K, Sharma M, Vig L. Meta-dermdiagnosis: Few-shot skin disease identification using meta-learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020;730\u2013731","DOI":"10.1109\/CVPRW50498.2020.00373"},{"issue":"1","key":"pone.0269365.ref015","first-page":"1","article-title":"Classification of femur fracture in pelvic X-ray images using meta-learned deep neural network","volume":"10","author":"C Lee","year":"2020","journal-title":"Scientific reports"},{"issue":"1","key":"pone.0269365.ref016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12879-018-3285-4","article-title":"Using Baidu index to nowcast hand-foot-mouth disease in China: a meta learning approach","volume":"18","author":"Y Zhao","year":"2018","journal-title":"BMC Infect Dis"},{"key":"pone.0269365.ref017","doi-asserted-by":"crossref","unstructured":"Lee K, Maji S, Ravichandran A, Soatto S. Meta-learning with differentiable convex optimization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2018;10657\u201310665.","DOI":"10.1109\/CVPR.2019.01091"},{"key":"pone.0269365.ref018","first-page":"3630","article-title":"Matching networks for one shot learning","author":"O Vinyals","year":"2016","journal-title":"In: Advances in Neural Information Processing Systems"},{"key":"pone.0269365.ref019","unstructured":"Ravi S, Larochelle H. Optimization as a model for few-shot learning. In: Proceedings of the International Conference on Learning Representations. 2017."},{"key":"pone.0269365.ref020","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2016;770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"pone.0269365.ref021","first-page":"10750","article-title":"Dropblock: A regularization method for convolutional networks","author":"G Ghias","year":"2018","journal-title":"In: Proceedings of the Neural Information Processing Systems"},{"issue":"1","key":"pone.0269365.ref022","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/001316446002000116","article-title":"The application of electronic computers to factor analysis","volume":"20","author":"HF Kaiser","year":"1960","journal-title":"Educ Psychol Meas"},{"key":"pone.0269365.ref023","first-page":"1755","article-title":"Dlib-ml: A machine learning toolkit","volume":"10","author":"DE King","year":"2009","journal-title":"J Mach Learn Res"},{"issue":"1","key":"pone.0269365.ref024","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"N Otsu","year":"1979","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"3","key":"pone.0269365.ref025","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/965139.807361","article-title":"Color gamut transform pairs","volume":"12","author":"AR Smith","year":"1978","journal-title":"Comput Graph"},{"issue":"3","key":"pone.0269365.ref026","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s10851-005-0482-8","article-title":"Least squares fitting of circles","volume":"23","author":"N Chernov","year":"2005","journal-title":"J. Math. Imaging Vis"},{"issue":"5","key":"pone.0269365.ref027","first-page":"699","article-title":"The Central Corneal Light Reflex Ratio from Photographs derived from a Digital Camera in Young Adults","volume":"95","author":"S Duangsang","year":"2012","journal-title":"J Med Assoc Thai"},{"issue":"2","key":"pone.0269365.ref028","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1167\/tvst.9.2.7","article-title":"A Clinician\u2019s Guide to Artificial Intelligence: How to Critically Appraise Machine Learning Studies","volume":"9","author":"L Faes","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"pone.0269365.ref029","doi-asserted-by":"crossref","unstructured":"Rosten E, Drummond T. Machine learning for high-speed corner detection. In: Proceedings of the European conference on computer vision. 2006;430\u2013443","DOI":"10.1007\/11744023_34"},{"key":"pone.0269365.ref030","doi-asserted-by":"crossref","unstructured":"Rublee E, Rabaud V, Konolige K, Bradski G. ORB: An efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision. 2011;2564\u20132571.","DOI":"10.1109\/ICCV.2011.6126544"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0269365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T17:31:33Z","timestamp":1659720693000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0269365"}},"subtitle":[],"editor":[{"given":"Jyotismita","family":"Chaki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":30,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8,5]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0269365","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,5]]}}}