{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:31:41Z","timestamp":1787841101493,"version":"build-2784847793"},"reference-count":81,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract:<\/jats:title>\n                  <jats:p>Glaucoma is an optic neuropathy and the leading cause of irreversible blindness worldwide. Imaging of the ganglion cell complex and retinal nerve fiber layer with optical coherence tomography (OCT) is a noninvasive, high-resolution means of diagnosing and quantitatively monitoring glaucoma. In the anterior segment, OCT can also be used to assess the anterior chamber angle and identify angle closure, a risk factor for glaucoma. The interpretation of OCT images for accurate diagnosis requires expert-level knowledge of both the technology and glaucoma. Deep learning (DL) is a subfield of artificial intelligence (AI), which is gaining prominence in health care for its ability to interpret images and approximate clinician judgment. This review summarizes recent research that demonstrates how DL can contribute to the analysis of OCT images in glaucoma. Deep neural networks can assist clinicians in checking the quality of OCT scans, quantifying the thickness of optic nerve tissues, evaluating the anterior chamber angle, diagnosing glaucoma, and detecting the progression of existing glaucoma. As further work expands on the generalizability, equity, and explainability of these DL techniques, AI-driven clinical support tools may become available for glaucoma diagnostics.<\/jats:p>","DOI":"10.4103\/tjo.tjo-d-24-00162","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T11:00:34Z","timestamp":1752836434000},"page":"354-363","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Applications of deep learning in the analysis of optical coherence tomography images for glaucoma-related diagnostics"],"prefix":"10.4103","volume":"15","author":[{"given":"Kyle","family":"Bolo","sequence":"first","affiliation":[{"name":"Keck School of Medicine, Roski Eye Institute, University of Southern California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin Y.","family":"Xu","sequence":"additional","affiliation":[{"name":"Keck School of Medicine, Roski Eye Institute, University of Southern California, Los Angeles, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"R1-20250915","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1136\/bjo.2005.081224","article-title":"The number of people with glaucoma worldwide in 2010 and 2020","volume":"90","author":"Quigley","year":"2006","journal-title":"Br J Ophthalmol"},{"key":"R2-20250915","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through. 2040: A systematic review and meta-analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"R3-20250915","doi-asserted-by":"crossref","first-page":"2002","DOI":"10.1016\/j.ophtha.2015.06.015","article-title":"Estimating lead time gained by optical coherence tomography in detecting glaucoma before development of visual field defects","volume":"122","author":"Kuang","year":"2015","journal-title":"Ophthalmology"},{"key":"R4-20250915","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"Huang","year":"1991","journal-title":"Science"},{"key":"R5-20250915","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1097\/00055735-199504000-00014","article-title":"Optical coherence tomography: A new tool for glaucoma diagnosis","volume":"6","author":"Schuman","year":"1995","journal-title":"Curr Opin Ophthalmol"},{"key":"R6-20250915","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1016\/S0161-6420(96)30410-7","article-title":"Reproducibility of nerve fiber layer thickness measurements using optical coherence tomography","volume":"103","author":"Schuman","year":"1996","journal-title":"Ophthalmology"},{"key":"R7-20250915","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1097\/ICU.0000000000000979","article-title":"Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision academy retinal expert consensus","volume":"34","author":"Chou","year":"2023","journal-title":"Curr Opin Ophthalmol"},{"key":"R8-20250915","doi-asserted-by":"crossref","first-page":"16067","DOI":"10.1038\/nrdp.2016.67","article-title":"Primary open-angle glaucoma","volume":"2","author":"Weinreb","year":"2016","journal-title":"Nat Rev Dis Primers"},{"key":"R9-20250915","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1097\/ICU.0b013e32835d9e27","article-title":"Diagnosis of glaucoma and detection of glaucoma progression using spectral domain optical coherence tomography","volume":"24","author":"Grewal","year":"2013","journal-title":"Curr Opin Ophthalmol"},{"key":"R10-20250915","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1002\/jbio.201500239","article-title":"Performance evaluation of automated segmentation software on optical coherence tomography volume data","volume":"9","author":"Tian","year":"2016","journal-title":"J Biophotonics"},{"key":"R11-20250915","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4103\/2228-7477.114321","article-title":"A review of algorithms for segmentation of optical coherence tomography from retina","volume":"3","author":"Kafieh","year":"2013","journal-title":"J Med Signals Sens"},{"key":"R12-20250915","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1016\/j.ophtha.2010.11.029","article-title":"Comparison of the diagnostic accuracies of the spectralis, cirrus, and RTVue optical coherence tomography devices in glaucoma","volume":"118","author":"Leite","year":"2011","journal-title":"Ophthalmology"},{"key":"R13-20250915","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.ophtha.2015.12.041","article-title":"Macular versus retinal nerve fiber layer parameters for diagnosing manifest glaucoma: A systematic review of diagnostic accuracy studies","volume":"123","author":"Oddone","year":"2016","journal-title":"Ophthalmology"},{"key":"R14-20250915","doi-asserted-by":"crossref","first-page":"5912","DOI":"10.1167\/iovs.11-8644","article-title":"Retinal nerve fiber layer thickness reproducibility using seven different OCT instruments","volume":"53","author":"Pierro","year":"2012","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"R15-20250915","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1167\/iovs.12-9786","article-title":"Longitudinal analysis of progression in glaucoma using spectral-domain optical coherence tomography","volume":"54","author":"Wessel","year":"2013","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"R16-20250915","doi-asserted-by":"crossref","first-page":"1281751","DOI":"10.3389\/fmed.2023.1281751","article-title":"Agreement, repeatability, and reproducibility of quantitative retinal layer assessment using swept-source and spectral-domain optical coherence tomography in eyes with retinal diseases","volume":"10","author":"Hou","year":"2023","journal-title":"Front Med (Lausanne)"},{"key":"R17-20250915","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1097\/ICU.0000000000000733","article-title":"Recent developments in the use of optical coherence tomography for glaucoma","volume":"32","author":"Vazquez","year":"2021","journal-title":"Curr Opin Ophthalmol"},{"key":"R18-20250915","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1167\/tvst.11.5.11","article-title":"Deep learning for glaucoma detection and identification of novel diagnostic areas in diverse real-world datasets","volume":"11","author":"Noury","year":"2022","journal-title":"Transl Vis Sci Technol"},{"key":"R19-20250915","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1167\/tvst.13.1.4","article-title":"Detecting established glaucoma using OCT alone: Utilizing an OCT reading center in a real-world clinical setting","volume":"13","author":"Leshno","year":"2024","journal-title":"Transl Vis Sci Technol"},{"key":"R20-20250915","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ajo.2016.10.020","article-title":"Automated segmentation errors when using optical coherence tomography to measure retinal nerve fiber layer thickness in glaucoma","volume":"174","author":"Mansberger","year":"2017","journal-title":"Am J Ophthalmol"},{"key":"R21-20250915","doi-asserted-by":"crossref","first-page":"9836","DOI":"10.1038\/s41598-019-46294-6","article-title":"Detecting retinal nerve fibre layer segmentation errors on spectral domain-optical coherence tomography with a deep learning algorithm","volume":"9","author":"Jammal","year":"2019","journal-title":"Sci Rep"},{"key":"R22-20250915","doi-asserted-by":"crossref","first-page":"106801","DOI":"10.1016\/j.cmpb.2022.106801","article-title":"Automatic segmentation of the optic nerve head region in optical coherence tomography: A methodological review","volume":"220","author":"Marques","year":"2022","journal-title":"Comput Methods Programs Biomed"},{"key":"R23-20250915","doi-asserted-by":"crossref","first-page":"41","DOI":"10.18502\/jovr.v18i1.12724","article-title":"Correction of retinal nerve fiber layer thickness measurement on spectral-domain optical coherence tomographic images using U-net architecture","volume":"18","author":"Razaghi","year":"2023","journal-title":"J Ophthalmic Vis Res"},{"key":"R24-20250915","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1167\/tvst.10.8.21","article-title":"Deep learning-based retinal nerve fiber layer thickness measurement of murine eyes","volume":"10","author":"Ma","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"R25-20250915","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1038\/s41598-019-57196-y","article-title":"Quantification of retinal nerve fibre layer thickness on optical coherence tomography with a deep learning segmentation-free approach","volume":"10","author":"Mariottoni","year":"2020","journal-title":"Sci Rep"},{"key":"R26-20250915","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1167\/tvst.10.9.16","article-title":"Estimating the severity of visual field damage from retinal nerve fiber layer thickness measurements with artificial intelligence","volume":"10","author":"Huang","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"R27-20250915","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.ophtha.2019.09.036","article-title":"Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps","volume":"127","author":"Christopher","year":"2020","journal-title":"Ophthalmology"},{"key":"R28-20250915","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ajo.2020.04.037","article-title":"Predicting the Glaucomatous central 10-degree visual field from optical coherence tomography using deep learning and tensor regression","volume":"218","author":"Xu","year":"2020","journal-title":"Am J Ophthalmol"},{"key":"R29-20250915","doi-asserted-by":"crossref","first-page":"100055","DOI":"10.1016\/j.xops.2021.100055","article-title":"Ajoint multitask learning model for cross-sectional and longitudinal predictions of visual field using OCT","volume":"1","author":"Asaoka","year":"2021","journal-title":"Ophthalmol Sci"},{"key":"R30-20250915","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ajo.2022.10.013","article-title":"Deep learning estimation of 10-2 visual field map based on circumpapillary retinal nerve fiber layer thickness measurements","volume":"246","author":"Kamalipour","year":"2023","journal-title":"Am J Ophthalmol"},{"key":"R31-20250915","doi-asserted-by":"crossref","first-page":"e0234902","DOI":"10.1371\/journal.pone.0234902","article-title":"A deep learning approach to predict visual field using optical coherence tomography","volume":"15","author":"Park","year":"2020","journal-title":"PLoS One"},{"key":"R32-20250915","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1167\/tvst.12.11.5","article-title":"Prediction of central visual field measures from macular OCT volume scans with deep learning","volume":"12","author":"Mohammadzadeh","year":"2023","journal-title":"Transl Vis Sci Technol"},{"key":"R33-20250915","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1167\/tvst.12.6.28","article-title":"Segmentation-free OCT-volume-based deep learning model improves pointwise visual field sensitivity estimation","volume":"12","author":"Chen","year":"2023","journal-title":"Transl Vis Sci Technol"},{"key":"R34-20250915","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ajo.2018.10.007","article-title":"Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images","volume":"198","author":"Asaoka","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"R35-20250915","doi-asserted-by":"crossref","first-page":"e0219126","DOI":"10.1371\/journal.pone.0219126","article-title":"A feature agnostic approach for glaucoma detection in OCT volumes","volume":"14","author":"Maetschke","year":"2019","journal-title":"PLoS One"},{"key":"R36-20250915","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TBME.2020.3043215","article-title":"Robust and interpretable convolutional neural networks to detect glaucoma in optical coherence tomography images","volume":"68","author":"Thakoor","year":"2021","journal-title":"IEEE Trans Biomed Eng"},{"key":"R37-20250915","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1097\/IJG.0000000000000765","article-title":"Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects","volume":"26","author":"Muhammad","year":"2017","journal-title":"J Glaucoma"},{"key":"R38-20250915","first-page":"2036","article-title":"Enhancing the accuracy of glaucoma detection from OCT probability maps using convolutional neural networks","volume":"2019","author":"Thakoor","year":"2019","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"R39-20250915","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1001\/jamaophthalmol.2019.5983","article-title":"Assessment of a segmentation-free deep learning algorithm for diagnosing glaucoma from optical coherence tomography scans","volume":"138","author":"Thompson","year":"2020","journal-title":"JAMA Ophthalmol"},{"key":"R40-20250915","doi-asserted-by":"crossref","first-page":"860574","DOI":"10.3389\/fmed.2022.860574","article-title":"Three-dimensional multi-task deep learning model to detect glaucomatous optic neuropathy and myopic features from optical coherence tomography scans: A retrospective multi-centre study","volume":"9","author":"Ran","year":"2022","journal-title":"Front Med (Lausanne)"},{"key":"R41-20250915","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":"R42-20250915","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1167\/tvst.9.2.12","article-title":"A3D deep learning system for detecting referable glaucoma using full OCT macular cube scans","volume":"9","author":"Russakoff","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"R43-20250915","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.artmed.2015.04.002","article-title":"Learning from healthy and stable eyes: A new approach for detection of glaucomatous progression","volume":"64","author":"Belghith","year":"2015","journal-title":"Artif Intell Med"},{"key":"R44-20250915","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1167\/tvst.10.8.19","article-title":"Individualized glaucoma change detection using deep learning auto encoder-based regions of interest","volume":"10","author":"Bowd","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"R45-20250915","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ogla.2022.11.004","article-title":"Deep learning-assisted detection of glaucoma progression in spectral-domain OCT","volume":"6","author":"Mariottoni","year":"2023","journal-title":"Ophthalmol Glaucoma"},{"key":"R46-20250915","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1136\/bjo.86.2.238","article-title":"The definition and classification of glaucoma in prevalence surveys","volume":"86","author":"Foster","year":"2002","journal-title":"Br J Ophthalmol"},{"key":"R47-20250915","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1097\/00061198-200304000-00013","article-title":"Possible mechanisms of primary angle-closure and malignant glaucoma","volume":"12","author":"Quigley","year":"2003","journal-title":"J Glaucoma"},{"key":"R48-20250915","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1001\/jama.2014.3192","article-title":"The pathophysiology and treatment of glaucoma: A review","volume":"311","author":"Weinreb","year":"2014","journal-title":"JAMA"},{"key":"R49-20250915","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1167\/tvst.12.1.16","article-title":"A mechanistic model of aqueous humor flow to study effects of angle closure on intraocular pressure","volume":"12","author":"Murgoitio-Esandi","year":"2023","journal-title":"Transl Vis Sci Technol"},{"key":"R50-20250915","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1136\/bjophthalmol-2021-319470","article-title":"Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure","volume":"107","author":"Randhawa","year":"2023","journal-title":"Br J Ophthalmol"},{"key":"R51-20250915","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.ajo.2024.02.032","article-title":"Patterns and disparities in recorded gonioscopy during initial glaucoma evaluations in the United States","volume":"264","author":"Lee","year":"2024","journal-title":"Am J Ophthalmol"},{"key":"R52-20250915","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ogla.2022.01.001","article-title":"Racial and sociodemographic disparities in the detection of narrow angles before detection of primary angle-closure glaucoma in the United States","volume":"5","author":"Apolo","year":"2022","journal-title":"Ophthalmol Glaucoma"},{"key":"R53-20250915","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.ajo.2024.12.008","article-title":"Corrigendum to \u201cPractice patterns and sociodemographic disparities in the clinical care of anatomical narrow angles in the United States\u201d [American Journal of Ophthalmology Volume 261 (2024) Pages 66-75]","volume":"272","author":"Yoo","year":"2025","journal-title":"Am J Ophthalmol"},{"key":"R54-20250915","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ajo.2023.11.007","article-title":"Prevalence and risk factors of blindness among primary angle closure glaucoma patients in the United States: An IRIS registry analysis","volume":"259","author":"Shah","year":"2024","journal-title":"Am J Ophthalmol"},{"key":"R55-20250915","first-page":"146","article-title":"Anterior segment optical coherence tomography: applications for clinical care and scientific research","volume":"8","author":"Shan","year":"2019","journal-title":"Asia Pac J Ophthalmol (Phila)"},{"key":"R56-20250915","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1167\/tvst.8.2.5","article-title":"Differences in anterior chamber angle assessments between gonioscopy, EyeCam, and anterior segment OCT: The Chinese American eye study","volume":"8","author":"Xu","year":"2019","journal-title":"Transl Vis Sci Technol"},{"key":"R57-20250915","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1167\/tvst.7.6.33","article-title":"Quantitative evaluation of gonioscopic and eyecam assessments of angle dimensions using anterior segment optical coherence tomography","volume":"7","author":"Xu","year":"2018","journal-title":"Transl Vis Sci Technol"},{"key":"R58-20250915","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1001\/archophthalmol.2007.46","article-title":"Assessment of the scleral spur in anterior segment optical coherence tomography images","volume":"126","author":"Sakata","year":"2008","journal-title":"Arch Ophthalmol"},{"key":"R59-20250915","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1136\/bjo-2022-322328","article-title":"Automated expert-level scleral spur detection and quantitative biometric analysis on the ANTERION anterior segment OCT system","volume":"108","author":"Bolo","year":"2024","journal-title":"Br J Ophthalmol"},{"key":"R60-20250915","doi-asserted-by":"crossref","first-page":"6313","DOI":"10.1167\/iovs.16-19755","article-title":"Benefit of measuring anterior segment structures using an increased number of optical coherence tomography images: The Chinese American eye study","volume":"57","author":"Xu","year":"2016","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"R61-20250915","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1136\/bjophthalmol-2021-319275","article-title":"Optimal number and orientation of anterior segment OCT images to measure ocular biometric parameters in angle closure eyes: The Chinese American eye study","volume":"107","author":"Shan","year":"2023","journal-title":"Br J Ophthalmol"},{"key":"R62-20250915","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.ophtha.2021.10.003","article-title":"Ocular biometric risk factors for progression of primary angle closure disease: The Zhongshan angle closure prevention trial","volume":"129","author":"Xu","year":"2022","journal-title":"Ophthalmology"},{"key":"R63-20250915","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ogla.2018.09.001","article-title":"Correlation between intraocular pressure and angle configuration measured by OCT: The Chinese American eye study","volume":"1","author":"Xu","year":"2018","journal-title":"Ophthalmol Glaucoma"},{"key":"R64-20250915","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ogla.2019.12.005","article-title":"Effect of angle narrowing on sectoral variation of anterior chamber angle width: The Chinese American eye study","volume":"3","author":"Xu","year":"2020","journal-title":"Ophthalmol Glaucoma"},{"key":"R65-20250915","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1167\/tvst.12.9.4","article-title":"Classification of angle closure severity by hierarchical cluster analysis of ocular biometrics in the dark and light","volume":"12","author":"Cho","year":"2023","journal-title":"Transl Vis Sci Technol"},{"key":"R66-20250915","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ogla.2020.09.008","article-title":"Differences in ocular biometric measurements among subtypes of primary angle closure disease: The Chinese American eye study","volume":"4","author":"Xu","year":"2021","journal-title":"Ophthalmol Glaucoma"},{"key":"R67-20250915","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.ophtha.2021.01.021","article-title":"Anatomic changes and predictors of angle widening after laser peripheral iridotomy: The Zhongshan angle closure prevention trial","volume":"128","author":"Xu","year":"2021","journal-title":"Ophthalmology"},{"key":"R68-20250915","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1001\/jamaophthalmol.2023.0937","article-title":"Biometric risk factors for angle closure progression after laser peripheral iridotomy","volume":"141","author":"Bao","year":"2023","journal-title":"JAMA Ophthalmol"},{"key":"R69-20250915","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1167\/tvst.9.2.18","article-title":"Deep neural network for scleral spur detection in anterior segment OCT images: The Chinese American eye study","volume":"9","author":"Xu","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"R70-20250915","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1136\/bjophthalmol-2019-315723","article-title":"Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images","volume":"105","author":"Pham","year":"2021","journal-title":"Br J Ophthalmol"},{"key":"R71-20250915","doi-asserted-by":"crossref","first-page":"100360","DOI":"10.1016\/j.xops.2023.100360","article-title":"Deep learning-based quantification of anterior segment OCT parameters","volume":"4","author":"Soh","year":"2024","journal-title":"Ophthalmol Sci"},{"key":"R72-20250915","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ajo.2019.02.028","article-title":"Adeep learning system for automated angle-closure detection in anterior segment optical coherence tomography images","volume":"203","author":"Fu","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"R73-20250915","doi-asserted-by":"crossref","first-page":"775711","DOI":"10.3389\/fmed.2021.775711","article-title":"Diagnostic performance of deep learning classifiers in measuring peripheral anterior synechia based on swept source optical coherence tomography images","volume":"8","author":"Yang","year":"2021","journal-title":"Front Med (Lausanne)"},{"key":"R74-20250915","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ogla.2023.06.011","article-title":"Deep learning classification of angle closure based on anterior segment OCT","volume":"7","author":"Shan","year":"2024","journal-title":"Ophthalmol Glaucoma"},{"key":"R75-20250915","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.ajo.2019.08.004","article-title":"Deep learning classifiers for automated detection of gonioscopic angle closure based on anterior segment OCT images","volume":"208","author":"Xu","year":"2019","journal-title":"Am J Ophthalmol"},{"key":"R76-20250915","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1136\/bjophthalmol-2021-319058","article-title":"Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure","volume":"107","author":"Shen","year":"2023","journal-title":"Br J Ophthalmol"},{"key":"R77-20250915","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1136\/bjophthalmol-2020-318275","article-title":"Towards 'automated gonioscopy': A deep learning algorithm for 360\u00b0angle assessment by swept-source optical coherence tomography","volume":"106","author":"Porporato","year":"2022","journal-title":"Br J Ophthalmol"},{"key":"R78-20250915","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ophtha.2021.09.018","article-title":"Digital gonioscopy based on three-dimensional anterior-segment OCT: An international multicenter study","volume":"129","author":"Li","year":"2022","journal-title":"Ophthalmology"},{"key":"R79-20250915","doi-asserted-by":"crossref","first-page":"e000931","DOI":"10.1136\/bmjophth-2021-000931","article-title":"Diagnostic performance of anterior segment optical coherence tomography in detecting plateau iris","volume":"7","author":"Wanichwecharungruang","year":"2022","journal-title":"BMJ Open Ophthalmol"},{"key":"R80-20250915","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1167\/tvst.10.1.7","article-title":"Deep learning for anterior segment optical coherence tomography to predict the presence of plateau iris","volume":"10","author":"Wanichwecharungruang","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"R81-20250915","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1167\/tvst.10.4.34","article-title":"Assessment of generative adversarial networks for synthetic anterior segment optical coherence tomography images in closed-angle detection","volume":"10","author":"Zheng","year":"2021","journal-title":"Transl Vis Sci Technol"}],"container-title":["Taiwan Journal of Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/tjo.TJO-D-24-00162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:42:23Z","timestamp":1787838143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/tjo.TJO-D-24-00162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":81,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.4103\/tjo.tjo-d-24-00162","relation":{},"ISSN":["2211-5056","2211-5072"],"issn-type":[{"value":"2211-5056","type":"print"},{"value":"2211-5072","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"2024-12-15","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-25","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}