{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T22:03:44Z","timestamp":1791583424109,"version":"4.3.4"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019R1A5A2027588"],"award-info":[{"award-number":["2019R1A5A2027588"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Pathologic myopia causes vision impairment and blindness, and therefore, necessitates a prompt diagnosis. However, there is no standardized definition of pathologic myopia, and its interpretation by 3D optical coherence tomography images is subjective, requiring considerable time and money. Therefore, there is a need for a diagnostic tool that can automatically and quickly diagnose pathologic myopia in patients. This study aimed to develop an algorithm that uses 3D optical coherence tomography volumetric images (C-scan) to automatically diagnose patients with pathologic myopia. The study was conducted using 367 eyes of patients who underwent optical coherence tomography tests at the Ophthalmology Department of Incheon St. Mary\u2019s Hospital and Seoul St. Mary\u2019s Hospital from January 2012 to May 2020. To automatically diagnose pathologic myopia, a deep learning model was developed using 3D optical coherence tomography images. The model was developed using transfer learning based on four pre-trained convolutional neural networks (ResNet18, ResNext50, EfficientNetB0, EfficientNetB4). Grad-CAM was used to visualize features affecting the detection of pathologic myopia. The performance of each model was evaluated and compared based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The model based on EfficientNetB4 showed the best performance (95% accuracy, 93% sensitivity, 96% specificity, and 98% AUROC) in identifying pathologic myopia.<\/jats:p>","DOI":"10.3390\/diagnostics12030742","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:26:22Z","timestamp":1647811582000},"page":"742","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3449-7532","authenticated-orcid":false,"given":"So-Jin","family":"Park","sequence":"first","affiliation":[{"name":"Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"},{"name":"Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4045-0036","authenticated-orcid":false,"given":"Taehoon","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"},{"name":"Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan-Kee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Seoul St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-2340","authenticated-orcid":false,"given":"Yong-Chan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Incheon St. Mary\u2019s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"In-Young","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"},{"name":"Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.ajo.2013.08.010","article-title":"Epidemiology and Disease Burden of Pathologic Myopia and Myopic Choroidal Neovascularization: An Evidence-Based Systematic Review","volume":"157","author":"Wong","year":"2013","journal-title":"Am. J. Ophthalmol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1167\/iovs.62.4.5","article-title":"IMI Pathologic Myopia","volume":"62","author":"Wu","year":"2021","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","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":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1001\/jamaophthalmol.2016.1261","article-title":"Prevalence and Causes of Visual Impairment and Blindness in Chinese American Adults: The Chinese American eye study","volume":"134","author":"Varma","year":"2016","journal-title":"JAMA Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1097\/IAE.0000000000001348","article-title":"What is the fundamental nature of pathologic myopia?","volume":"37","year":"2017","journal-title":"Retina"},{"key":"ref_6","first-page":"80","article-title":"Myopic maculopathy: Current status and proposal for a new classification and grading system (ATN)","volume":"69","author":"Montero","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"M1","DOI":"10.1167\/iovs.18-25980","article-title":"IMI\u2014Myopia Control Reports Overview and Introduction","volume":"60","author":"Wolffsohn","year":"2019","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1626","DOI":"10.1016\/j.ophtha.2011.01.018","article-title":"Topographic Analyses of Shape of Eyes with Pathologic Myopia by High-Resolution Three-Dimensional Magnetic Resonance Imaging","volume":"118","author":"Moriyama","year":"2011","journal-title":"Ophthalmology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.ajo.2015.11.016","article-title":"Clinical Characteristics of Posterior Staphylomas in Myopic Eyes with Axial Length Shorter Than 26.5 Millimeters","volume":"162","author":"Wang","year":"2015","journal-title":"Am. J. Ophthalmol."},{"key":"ref_10","doi-asserted-by":"crossref","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":"ref_11","doi-asserted-by":"crossref","first-page":"6950","DOI":"10.1038\/s41598-021-85699-0","article-title":"Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera","volume":"11","author":"Kim","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","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. Retin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e23472","DOI":"10.2196\/23472","article-title":"Deep Learning\u2014Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation","volume":"8","author":"Kang","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e172","DOI":"10.1016\/S2589-7500(19)30085-8","article-title":"Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: A retrospective training and validation deep-learning analysis","volume":"1","author":"Ran","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1167\/tvst.10.6.32","article-title":"Assessing the Clinical Utility of Expanded Macular OCTs Using Machine Learning","volume":"10","author":"Lin","year":"2021","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s11517-021-02321-1","article-title":"Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification","volume":"59","author":"Yoo","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"Ledsam","year":"2018","journal-title":"Nat. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Maetschke, S., Antony, B., Ishikawa, H., Wollstein, G., Schuman, J., and Garnavi, R. (2019). A feature agnostic approach for glaucoma detection in OCT volumes. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0219126"},{"key":"ref_19","first-page":"110","article-title":"Optical Coherence Tomographic Findings in Highly Myopic Eyes","volume":"5","author":"Faghihi","year":"2010","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"M20","DOI":"10.1167\/iovs.18-25957","article-title":"IMI\u2014Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies","volume":"60","author":"Flitcroft","year":"2019","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.ajo.2015.01.022","article-title":"International Photographic Classification and Grading System for Myopic Maculopathy","volume":"159","author":"Kawasaki","year":"2015","journal-title":"Am. J. Ophthalmol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/j.ophtha.2019.01.012","article-title":"OCT-Based Diagnostic Criteria for Different Stages of Myopic Maculopathy","volume":"126","author":"Fang","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_23","first-page":"99","article-title":"Posterior staphyloma in pathologic myopia","volume":"70","author":"Jonas","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"312974","DOI":"10.1155\/2013\/312974","article-title":"Clinical Applications of High Resolution In-Vivo Retinal Imaging","volume":"2013","author":"Choi","year":"2013","journal-title":"J. Ophthalmol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yao, P., Shen, S., Xu, M., Liu, P., Zhang, F., Xing, J., Shao, P., Kaffenberger, B., and Xu, R.X. (2021). Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification. IEEE Trans. Med. Imaging, online ahead of print.","DOI":"10.1109\/TMI.2021.3136682"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., and Le, Q.V. (2020, January 14\u201319). Randaugment: Practical automated data augmentation with a reduced search space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102564","DOI":"10.1016\/j.bspc.2021.102564","article-title":"Considering breast density for the classification of benign and malignant mammograms","volume":"67","author":"Huang","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A survey on deep transfer learning. Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e19569","DOI":"10.2196\/19569","article-title":"COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework with a Single Chest CT Image: Model Development and Validation","volume":"22","author":"Ko","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., and Satoh, Y. (2017, January 22\u201329). Learning spatio-temporal features with 3d residual networks for action recognition. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.373"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_33","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","volume":"2019","author":"Tan","year":"2019","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_34","unstructured":"Clevert, D.-A., Unterthiner, T., and Hochreiter, S. (2015). Fast and accurate deep network learning by exponential linear units (elus). arXiv."},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_37","unstructured":"(2022, January 26). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_38","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106236","DOI":"10.1016\/j.cmpb.2021.106236","article-title":"TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning","volume":"208","author":"Sparks","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_40","unstructured":"Bradski, G., and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O\u2019Reilly Media Inc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106691","DOI":"10.1016\/j.asoc.2020.106691","article-title":"Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network","volume":"96","author":"Marques","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1038\/eye.2016.47","article-title":"Advances of optical coherence tomography in myopia and pathologic myopia","volume":"30","author":"Ng","year":"2016","journal-title":"Eye"},{"key":"ref_43","first-page":"1","article-title":"Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images","volume":"201","author":"Yoo","year":"2021","journal-title":"Eye"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1167\/iovs.19-26794","article-title":"Impact of Posterior Sclera on Glaucoma Progression in Treated Myopic Normal-Tension Glaucoma Using Reconstructed Optical Coherence Tomographic Images","volume":"60","author":"Kim","year":"2019","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ajo.2007.09.022","article-title":"En Face Optical Coherence Tomography of the Posterior Pole in High Myopia","volume":"145","author":"Forte","year":"2008","journal-title":"Am. J. Ophthalmol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e28868","DOI":"10.2196\/28868","article-title":"A Multimodal Imaging\u2013Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study","volume":"9","author":"Kang","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wong, Y.L., Man, R.E.K., Fenwick, E., Saw, S.M., Wong, C.W., Cheung, C.M.G., and Lamoureux, E.L. (2021). Public Health Impact of Pathologic Myopia. Pathologic Myopia, Springer.","DOI":"10.1007\/978-3-030-74334-5_5"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/3\/742\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:39:00Z","timestamp":1760135940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/3\/742"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":47,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["diagnostics12030742"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12030742","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}