{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T11:45:05Z","timestamp":1794051905106,"version":"4.8.0"},"reference-count":42,"publisher":"Elsevier BV","issue":"7","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["aaojournal.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Ophthalmology"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.ophtha.2022.02.017","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T19:18:55Z","timestamp":1645471135000},"page":"781-791","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":65,"title":["Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging"],"prefix":"10.1016","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3902-7222","authenticated-orcid":false,"given":"Yuka","family":"Kihara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9148-2804","authenticated-orcid":false,"given":"Giovanni","family":"Montesano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6688-7674","authenticated-orcid":false,"given":"Andrew","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nishani","family":"Amerasinghe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chrysostomos","family":"Dimitriou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aby","family":"Jacob","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Almira","family":"Chabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David P.","family":"Crabb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron Y.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ophtha.2022.02.017_bib1","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1136\/bjo.82.4.352","article-title":"Quantitative evaluation of the optic nerve head in early glaucoma","volume":"82","author":"Garway-Heath","year":"1998","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib2","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":"10.1016\/j.ophtha.2022.02.017_bib3","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/S0161-6420(82)34798-3","article-title":"The onset and evolution of glaucomatous\u00a0visual field defects","volume":"89","author":"Hart","year":"1982","journal-title":"Ophthalmology"},{"key":"10.1016\/j.ophtha.2022.02.017_bib4","first-page":"2654","article-title":"Properties of perimetric threshold estimates from Full Threshold, SITA Standard, and SITA Fast strategies","volume":"43","author":"Artes","year":"2002","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib5","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1167\/iovs.03-0023","article-title":"Properties of perimetric threshold estimates from full threshold, ZEST, and SITA-like strategies, as determined by computer simulation","volume":"44","author":"Turpin","year":"2003","journal-title":"Invest Opthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib6","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":"10.1016\/j.ophtha.2022.02.017_bib9","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1016\/S0161-6420(00)00284-0","article-title":"Mapping the visual field to the optic disc in normal tension glaucoma eyes","volume":"107","author":"Garway-Heath","year":"2000","journal-title":"Ophthalmology"},{"key":"10.1016\/j.ophtha.2022.02.017_bib10","doi-asserted-by":"crossref","first-page":"3249","DOI":"10.1167\/iovs.08-2492","article-title":"Combining ganglion cell topology and data of patients with glaucoma to determine a structure\u2013function map","volume":"50","author":"Turpin","year":"2009","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib29","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.exer.2012.10.008","article-title":"A\u00a0mathematical model for describing the retinal nerve fiber bundle trajectories in the human eye: average course, variability, and influence of refraction, optic disc size and optic disc position","volume":"105","author":"Jansonius","year":"2012","journal-title":"Exp Eye Res"},{"key":"10.1016\/j.ophtha.2022.02.017_bib15","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1016\/j.visres.2009.04.029","article-title":"A\u00a0mathematical description of nerve fiber bundle trajectories and their variability in the human retina","volume":"49","author":"Jansonius","year":"2009","journal-title":"Vision Res"},{"key":"10.1016\/j.ophtha.2022.02.017_bib11","doi-asserted-by":"crossref","first-page":"6074","DOI":"10.1167\/iovs.13-11902","article-title":"The influence of intersubject variability in ocular anatomical variables on the mapping of retinal locations to the retinal nerve fiber layer and optic nerve head","volume":"54","author":"Lamparter","year":"2013","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib12","doi-asserted-by":"crossref","first-page":"6981","DOI":"10.1167\/iovs.12-9657","article-title":"An anatomically customizable computational model relating the visual field to the optic nerve head in individual eyes","volume":"53","author":"Denniss","year":"2012","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib13","doi-asserted-by":"crossref","first-page":"6320","DOI":"10.1167\/iovs.15-17450","article-title":"Influence of the retinal blood vessel topography on the variability of the retinal nerve fiber bundle trajectories in the human retina","volume":"56","author":"Qiu","year":"2015","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1167\/tvst.10.1.19","article-title":"Improving personalized structure to function mapping from optic nerve head to visual field","volume":"10","author":"Turpin","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib16","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.preteyeres.2007.08.001","article-title":"A\u00a0framework for comparing structural and functional measures of glaucomatous damage","volume":"26","author":"Hood","year":"2007","journal-title":"Prog Retin Eye Res"},{"key":"10.1016\/j.ophtha.2022.02.017_bib17","doi-asserted-by":"crossref","first-page":"5657","DOI":"10.1167\/iovs.10-5239","article-title":"Predicting visual function from the measurements of retinal nerve fiber layer structure","volume":"51","author":"Zhu","year":"2010","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib18","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1136\/bjophthalmol-2019-315600","article-title":"Deep learning model to predict visual field in central 10\u00b0 from optical coherence tomography measurement in glaucoma","volume":"105","author":"Hashimoto","year":"2021","journal-title":"Br J Ophthalmol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib19","article-title":"A\u00a0deep learning approach to predict visual field using optical coherence tomography","volume":"15","author":"Park","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.ophtha.2022.02.017_bib20","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ogla.2020.01.001","article-title":"Improving the structure-function relationship in glaucomatous visual fields by using a deep learning-based noise reduction approach","volume":"3","author":"Asaoka","year":"2020","journal-title":"Ophthalmol Glaucoma"},{"key":"10.1016\/j.ophtha.2022.02.017_bib21","doi-asserted-by":"crossref","DOI":"10.1167\/tvst.9.2.19","article-title":"Artificial intelligence mapping of structure to function in glaucoma","volume":"9","author":"Mariottoni","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1167\/tvst.10.7.4","article-title":"Visual field inference from optical coherence tomography using deep learning algorithms: a comparison between devices","volume":"10","author":"Shin","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-91493-9","article-title":"RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure","volume":"11","author":"Datta","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.ophtha.2022.02.017_bib24","first-page":"2201","article-title":"False-negative responses in glaucoma perimetry: indicators of patient performance or test reliability?","volume":"41","author":"Bengtsson","year":"2000","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib25","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1016\/j.ophtha.2017.04.035","article-title":"Evidence-based criteria for assessment of visual field reliability","volume":"124","author":"Yohannan","year":"2017","journal-title":"Ophthalmology"},{"key":"10.1016\/j.ophtha.2022.02.017_bib26","series-title":"The Field Analyzer Primer: Effective Perimetry","author":"Heijl","year":"2012"},{"key":"10.1016\/j.ophtha.2022.02.017_bib27","series-title":"International Conference on Machine Learning","first-page":"6105","article-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.ophtha.2022.02.017_bib28","author":"Dozat"},{"key":"10.1016\/j.ophtha.2022.02.017_bib30","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/BF00164646","article-title":"Temporal visual field in glaucoma: a re-evaluation in the automated perimetry era","volume":"230","author":"Pennebaker","year":"1992","journal-title":"Graefes Arch Clin Exp Ophthalmol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib31","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1097\/IJG.0000000000001427","article-title":"Temporal wedge defects in glaucoma: structure\/function correlation with threshold automated perimetry of the full visual field","volume":"29","author":"Wall","year":"2020","journal-title":"J\u00a0Glaucoma"},{"key":"10.1016\/j.ophtha.2022.02.017_bib32","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1016\/j.visres.2007.01.007","article-title":"The length of Henle fibers in the human retina and a model of ganglion receptive field density in the visual field","volume":"47","author":"Drasdo","year":"2007","journal-title":"Vision Res"},{"key":"10.1016\/j.ophtha.2022.02.017_bib33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1167\/tvst.9.10.15","article-title":"Revisiting the Drasdo model: implications for structure-function analysis of the macular region","volume":"9","author":"Montesano","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib34","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1167\/iovs.11-8436","article-title":"The effect of glaucoma on the optical attenuation coefficient of the retinal nerve fiber layer in spectral domain optical coherence tomography images","volume":"53","author":"van der Schoot","year":"2012","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib35","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":"10.1016\/j.ophtha.2022.02.017_bib36","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ogla.2020.07.002","article-title":"Estimating global visual field indices in glaucoma by combining macula and optic disc OCT scans using 3-dimensional convolutional neural networks","volume":"4","author":"Yu","year":"2021","journal-title":"Ophthalmol Glaucoma"},{"key":"10.1016\/j.ophtha.2022.02.017_bib37","doi-asserted-by":"crossref","first-page":"6208","DOI":"10.1167\/iovs.15-17259","article-title":"Details of glaucomatous damage are better seen on OCT en face images than on OCT retinal nerve fiber layer thickness maps","volume":"56","author":"Hood","year":"2015","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib38","doi-asserted-by":"crossref","first-page":"21052","DOI":"10.1038\/s41598-020-78144-1","article-title":"Estimating visual field loss from monoscopic optic disc photography using deep learning model","volume":"10","author":"Lee","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.ophtha.2022.02.017_bib39","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1167\/iovs.17-21832","article-title":"Optical coherence tomography analysis based prediction of Humphrey 24-2 visual field thresholds in patients with glaucoma","volume":"58","author":"Guo","year":"2017","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib40","doi-asserted-by":"crossref","first-page":"2760","DOI":"10.1167\/iovs.11-7976","article-title":"Improved estimates of visual field progression using Bayesian linear regression to integrate structural information in patients with ocular hypertension","volume":"53","author":"Russell","year":"2012","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1167\/tvst.2.4.3","article-title":"Towards patient-tailored perimetry: automated perimetry can be improved by seeding procedures with patient-specific structural information","volume":"2","author":"Denniss","year":"2013","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib42","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1167\/tvst.7.6.36","article-title":"Improving visual field examination of the macula using structural information","volume":"7","author":"Montesano","year":"2018","journal-title":"Transl Vis Sci Technol"},{"key":"10.1016\/j.ophtha.2022.02.017_bib43","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1097\/OPX.0000000000000447","article-title":"A\u00a0perimetric test procedure that uses structural information","volume":"92","author":"Ganeshrao","year":"2015","journal-title":"Optom Vis Sci"},{"key":"10.1016\/j.ophtha.2022.02.017_bib44","first-page":"10","article-title":"The visualFields package: a tool for analysis and visualization of visual fields","volume":"13","author":"Mar\u00edn-Franch","year":"2013","journal-title":"J\u00a0Vis"}],"container-title":["Ophthalmology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0161642022001567?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0161642022001567?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T14:11:22Z","timestamp":1758982282000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0161642022001567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["S0161642022001567"],"URL":"https:\/\/doi.org\/10.1016\/j.ophtha.2022.02.017","relation":{},"ISSN":["0161-6420"],"issn-type":[{"value":"0161-6420","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging","name":"articletitle","label":"Article Title"},{"value":"Ophthalmology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ophtha.2022.02.017","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 by the American Academy of Ophthalmology","name":"copyright","label":"Copyright"}]}}