{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:25:54Z","timestamp":1784827554046,"version":"3.55.0"},"reference-count":52,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T00:00:00Z","timestamp":1668988800000},"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":["2020R1A2B5B02001987"],"award-info":[{"award-number":["2020R1A2B5B02001987"]}],"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":["SRFC-IT1502-54"],"award-info":[{"award-number":["SRFC-IT1502-54"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Samsung Research Funding Center of Samsung Electronics","award":["2020R1A2B5B02001987"],"award-info":[{"award-number":["2020R1A2B5B02001987"]}]},{"name":"Samsung Research Funding Center of Samsung Electronics","award":["SRFC-IT1502-54"],"award-info":[{"award-number":["SRFC-IT1502-54"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and\/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma.<\/jats:p>","DOI":"10.3390\/diagnostics12112894","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T03:04:20Z","timestamp":1669086260000},"page":"2894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8384-8119","authenticated-orcid":false,"given":"Hyunmo","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-8773","authenticated-orcid":false,"given":"Yujin","family":"Ahn","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea"},{"name":"Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-0814","authenticated-orcid":false,"given":"Sanzhar","family":"Askaruly","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon S.","family":"You","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea"},{"name":"Incipian LLC, Laguna Niguel, CA 92677, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5998-5217","authenticated-orcid":false,"given":"Sang Woo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woonggyu","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1016\/S0140-6736(04)16257-0","article-title":"Primary open-angle glaucoma","volume":"363","author":"Weinreb","year":"2004","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1000.e1007","DOI":"10.1016\/j.amjmed.2016.03.038","article-title":"Emerging Concepts in Glaucoma and Review of the Literature","volume":"129","author":"Greco","year":"2016","journal-title":"Am. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41433-018-0042-2","article-title":"Normal tension glaucoma: Review of current understanding and mechanisms of the pathogenesis","volume":"32","author":"Killer","year":"2018","journal-title":"Eye"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.compmedimag.2013.09.005","article-title":"Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review","volume":"37","author":"Haleem","year":"2013","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1111\/ceo.13138","article-title":"Evolution of optic nerve photography for glaucoma screening: A review","volume":"46","author":"Myers","year":"2018","journal-title":"Clin. Exp. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.media.2009.12.006","article-title":"Glaucoma risk index:Automated glaucoma detection from color fundus images","volume":"14","author":"Bock","year":"2010","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.ajo.2007.07.010","article-title":"Optic Disk and Nerve Fiber Layer Imaging to Detect Glaucoma","volume":"144","author":"Raoof","year":"2007","journal-title":"Am. J. Ophthalmol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TMI.2009.2033909","article-title":"Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs","volume":"29","author":"Niemeijer","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jin, K., Lu, H., Su, Z., Cheng, C., Ye, J., and Qian, D. (2017). Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmol., 17.","DOI":"10.1186\/s12886-017-0484-5"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1038\/s41433-021-01485-2","article-title":"Validation of a portable, non-mydriatic fundus camera compared to gold standard dilated fundus examination using slit lamp biomicroscopy for assessing the optic disc for glaucoma","volume":"36","author":"Upadhyaya","year":"2021","journal-title":"Eye"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.1167\/iovs.11-9309","article-title":"Influence of Clinically Invisible, but Optical Coherence Tomography Detected, Optic Disc Margin Anatomy on Neuroretinal Rim Evaluation","volume":"53","author":"Reis","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1097\/ICU.0b013e32835f8bf8","article-title":"Optical coherence tomography--current and future applications","volume":"24","author":"Adhi","year":"2013","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1038\/sj.eye.6700729","article-title":"Optical coherence tomography\u2014A review of the principles and contemporary uses in retinal investigation","volume":"18","author":"Thomas","year":"2004","journal-title":"Eye"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/j.ajo.2011.09.032","article-title":"Diagnostic capability of spectral-domain optical coherence tomography for glaucoma","volume":"153","author":"Wu","year":"2012","journal-title":"Am. J. Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"041204","DOI":"10.1117\/1.2773736","article-title":"Minimum distance mapping using three-dimensional optical coherence tomography for glaucoma diagnosis","volume":"12","author":"Povazay","year":"2007","journal-title":"J. Biomed. Opt."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"ii15","DOI":"10.1136\/bjophthalmol-2013-304326","article-title":"OCT for glaucoma diagnosis, screening and detection of glaucoma progression","volume":"98","author":"Bussel","year":"2014","journal-title":"Br. J. Ophthalmol."},{"key":"ref_17","first-page":"254","article-title":"Spectral domain optical coherence tomography in glaucoma: Qualitative and quantitative analysis of the optic nerve head and retinal nerve fiber layer (an AOS thesis)","volume":"107","author":"Chen","year":"2009","journal-title":"Trans. Am. Ophthalmol. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.jcjo.2011.09.006","article-title":"Spectral-domain optical coherence tomography for early glaucoma assessment: Analysis of macular ganglion cell complex versus peripapillary retinal nerve fiber layer","volume":"46","author":"Moreno","year":"2011","journal-title":"Can. J. Ophthalmol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4974","DOI":"10.1167\/iovs.07-0342","article-title":"Prevalence of glaucoma in a South brazilian population: Projeto Glaucoma","volume":"48","author":"Sakata","year":"2007","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., and Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179790"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10750","DOI":"10.1038\/s41598-019-47181-w","article-title":"Deep learning fundus image analysis for diabetic retinopathy and macular edema grading","volume":"9","author":"Sahlsten","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","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":"ref_23","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_24","unstructured":"Raju, M., Pagidimarri, V., Barreto, R., Kadam, A., Kasivajjala, V., and Aswath, A. (2017). Development of a deep learning algorithm for automatic diagnosis of diabetic retinopathy. MEDINFO 2017: Precision Healthcare through Informatics, IOS Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1016\/j.ophtha.2018.01.023","article-title":"Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs","volume":"125","author":"Li","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101758","DOI":"10.1016\/j.artmed.2019.101758","article-title":"Ophthalmic diagnosis using deep learning with fundus images\u2013a critical review","volume":"102","author":"Sengupta","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Christopher, M., Hoseini, P., Walker, E., Proudfoot, J.A., Bowd, C., Fazio, M.A., Girkin, C.A., De Moraes, C.G., Liebmann, J.M., and Weinreb, R.N. (2022). A deep learning approach to improve retinal structural predictions and aid glaucoma neuroprotective clinical trial design. Ophthalmol. Glaucoma.","DOI":"10.1016\/j.ogla.2022.08.014"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ali, R., Hardie, R.C., Narayanan, B.N., and Kebede, T.M. (2022). IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. Appl. Sci., 12.","DOI":"10.3390\/app12115500"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.ajo.2019.11.006","article-title":"Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs","volume":"211","author":"Jammal","year":"2020","journal-title":"Am. J. Ophthalmol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ajo.2008.07.023","article-title":"Agreement among glaucoma specialists in assessing progressive disc changes from photographs in open-angle glaucoma patients","volume":"147","author":"Jampel","year":"2009","journal-title":"Am. J. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/S0161-6420(92)31990-6","article-title":"Expert agreement in evaluating the optic disc for glaucoma","volume":"99","author":"Varma","year":"1992","journal-title":"Ophthalmology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/S0161-6420(88)33177-5","article-title":"Intraobserver and interobserver agreement in measurement of optic disc characteristics","volume":"95","author":"Tielsch","year":"1988","journal-title":"Ophthalmology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/j.ophtha.2018.12.033","article-title":"From machine to machine: An OCT-trained deep learning algorithm for objective quantification of glaucomatous damage in fundus photographs","volume":"126","author":"Medeiros","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.ajo.2019.01.011","article-title":"A deep learning algorithm to quantify neuroretinal rim loss from optic disc photographs","volume":"201","author":"Thompson","year":"2019","journal-title":"Am. J. Ophthalmol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ajo.2018.10.017","article-title":"Prevalence of normal-tension glaucoma in the Chinese population: A systematic review and meta-analysis","volume":"199","author":"Zhao","year":"2019","journal-title":"Am. J. Ophthalmol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s10384-012-0153-4","article-title":"Risk factors for primary open-angle glaucoma in South Korea: The Namil study","volume":"56","author":"Kim","year":"2012","journal-title":"Jpn. J. Ophthalmol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1097\/ICU.0b013e3282f3919b","article-title":"Normal-tension glaucoma: Is it different from primary open-angle glaucoma?","volume":"19","author":"Shields","year":"2008","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1136\/bjo.87.6.695","article-title":"Comparison of localised nerve fibre layer defects in normal tension glaucoma and primary open angle glaucoma","volume":"87","author":"Woo","year":"2003","journal-title":"Br. J. Ophthalmol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1097\/IJG.0b013e318193c45c","article-title":"Normal versus high tension glaucoma: A comparison of functional and structural defects","volume":"19","author":"Thonginnetra","year":"2010","journal-title":"J. Glaucoma"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1038\/eye.2009.209","article-title":"Patterns of progression of localized retinal nerve fibre layer defect on red-free fundus photographs in normal-tension glaucoma","volume":"24","author":"Suh","year":"2010","journal-title":"Eye"},{"key":"ref_41","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"ref_42","unstructured":"Ioffe, S., and Szegedy, C. (2021, January 18\u201324). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of International Conference on Machine Learning, Virtual Event."},{"key":"ref_43","unstructured":"Nair, V., and Hinton, G.E. (2022, January 17\u201323). Rectified linear units improve restricted boltzmann machines. In Proceedings of ICML, Baltimore, MD, USA."},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1097\/IJG.0b013e3181c4b0c7","article-title":"Retinal nerve fiber layer thickness in normals measured by spectral domain OCT","volume":"19","author":"Bendschneider","year":"2010","journal-title":"J. Glaucoma"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"849","DOI":"10.2147\/OPTH.S155229","article-title":"The development of a reference database with the Topcon 3D OCT-1 Maestro","volume":"12","author":"Chaglasian","year":"2018","journal-title":"Clin. Ophthalmol. (Auckl. NZ)"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ajo.2019.11.002","article-title":"Ganglion cell-inner plexiform layer and retinal nerve fiber layer changes in glaucoma suspects enable prediction of glaucoma development","volume":"210","author":"Shin","year":"2020","journal-title":"Am. J. Ophthalmol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1111\/cxo.12656","article-title":"Glaucomatous progression in the retinal nerve fibre and retinal ganglion cell-inner plexiform layers determined using optical coherence tomography-guided progression analysis","volume":"101","author":"Hwang","year":"2018","journal-title":"Clin. Exp. Optom."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1016\/j.ophtha.2017.03.013","article-title":"Trend-based analysis of ganglion cell\u2013inner plexiform layer thickness changes on optical coherence tomography in glaucoma progression","volume":"124","author":"Lee","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.ophtha.2020.07.045","article-title":"Detection of progressive glaucomatous optic nerve damage on fundus photographs with deep learning","volume":"128","author":"Medeiros","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1016\/j.ophtha.2019.07.024","article-title":"Deep learning and glaucoma specialists: The relative importance of optic disc features to predict glaucoma referral in fundus photographs","volume":"126","author":"Phene","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.ogla.2021.09.002","article-title":"Glaucoma Screening Using an iPad-Based Visual Field Test in a West African Population","volume":"5","author":"Prince","year":"2022","journal-title":"Ophthalmol. Glaucoma"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/11\/2894\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:23:11Z","timestamp":1760145791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/11\/2894"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,21]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["diagnostics12112894"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12112894","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,21]]}}}