{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:18:47Z","timestamp":1783945127446,"version":"3.55.0"},"reference-count":27,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"7","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Trans. Vis. Sci. Tech."],"published-print":{"date-parts":[[2026,7,13]]},"DOI":"10.1167\/tvst.15.7.17","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:00:48Z","timestamp":1783944048000},"page":"17","source":"Crossref","is-referenced-by-count":0,"title":["Effect of Signal-Strength Filtering on 3D Convolutional Neural Network\u2013Based Visual Field Estimation From Macular OCT"],"prefix":"10.1167","volume":"15","author":[{"given":"Makoto","family":"Koyama","sequence":"first","affiliation":[{"name":"Minamikoyasu Eye Clinic, Kimitsu, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidenori","family":"Takahashi","sequence":"additional","affiliation":[{"name":"Center for Cyber Medicine Research, Institute of Medicine, University of Tsukuba, Tsukuba, Japan"},{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Inoda","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Jichi Medical University, Shimotsuke, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chihiro","family":"Mayama","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Japan Community Health Care Organization Tokyo Shinjuku Medical Center, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuta","family":"Ueno","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshikazu","family":"Ito","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuta","family":"Nariya","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Tokyo Hospital, Tokyo, Japan"},{"name":"Kameda Medical Center, Kamogawa, Chiba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuji","family":"Inoue","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Teikyo University, Itabashi-ku, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Kashiwagi","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, University of Yamanashi Faculty of Medicine, Yamanashi, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuro","family":"Oshika","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masaki","family":"Tanito","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Shimane University Faculty of Medicine, Izumo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"issue":"7","key":"bib1","doi-asserted-by":"publisher","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"},{"issue":"6","key":"bib2","doi-asserted-by":"publisher","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"},{"issue":"1","key":"bib3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1167\/tvst.14.1.22","article-title":"Explainable deep learning for glaucomatous visual field prediction: artifact correction enhances transformer models","volume":"14","author":"Sriwatana","year":"2025","journal-title":"Transl Vis Sci Technol"},{"issue":"1","key":"bib4","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1111\/nyas.14844","article-title":"Comparison of machine learning approaches for structure-function modeling in glaucoma","volume":"1515","author":"Wong","year":"2022","journal-title":"Ann N Y Acad Sci"},{"issue":"1","key":"bib5","doi-asserted-by":"publisher","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"},{"issue":"12","key":"bib6","doi-asserted-by":"publisher","first-page":"3421","DOI":"10.1109\/JBHI.2020.3001019","article-title":"Attention-guided 3D-CNN framework for glaucoma detection and structural-functional association using volumetric images","volume":"24","author":"George","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"bib7","doi-asserted-by":"publisher","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"},{"issue":"1","key":"bib8","doi-asserted-by":"publisher","first-page":"21041","DOI":"10.1038\/s41598-022-25660-x","article-title":"Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma","volume":"12","author":"Moon","year":"2022","journal-title":"Sci Rep"},{"issue":"9","key":"bib9","doi-asserted-by":"publisher","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":"bib10","doi-asserted-by":"publisher","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"},{"issue":"11","key":"bib11","doi-asserted-by":"publisher","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"},{"issue":"2","key":"bib12","doi-asserted-by":"publisher","first-page":"19","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"},{"issue":"4","key":"bib13","doi-asserted-by":"publisher","first-page":"100055","DOI":"10.1016\/j.xops.2021.100055","article-title":"A joint multitask learning model for cross-sectional and longitudinal predictions of visual field using OCT","volume":"1","author":"Asaoka","year":"2021","journal-title":"Ophthalmol Sci"},{"issue":"13","key":"bib14","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1167\/tvst.10.13.28","article-title":"Predicting 10-2 visual field from optical coherence tomography in glaucoma using deep learning corrected with 24-2\/30-2 visual field","volume":"10","author":"Hashimoto","year":"2021","journal-title":"Transl Vis Sci Technol"},{"key":"bib15","doi-asserted-by":"publisher","first-page":"13395","DOI":"10.1038\/s41598-025-98511-0","article-title":"Automated learning of glaucomatous visual fields from OCT images using a comprehensive, segmentation-free 3D convolutional neural network model","volume":"15","author":"Koyama","year":"2025","journal-title":"Sci Rep"},{"key":"bib17","doi-asserted-by":"publisher","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"},{"issue":"2","key":"bib18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.ophtha.2019.08.027","article-title":"Evidence-based criteria for determining peripapillary OCT reliability","volume":"127","author":"Yohannan","year":"2020","journal-title":"Ophthalmology"},{"issue":"11","key":"bib19","doi-asserted-by":"publisher","first-page":"7297","DOI":"10.1167\/iovs.14-14993","article-title":"A comprehensive model for correcting RNFL readings of varying signal strengths in Cirrus optical coherence tomography","volume":"55","author":"Russell","year":"2014","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"5","key":"bib20","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1167\/tvst.4.5.7","article-title":"Effect of signal intensity on measurement of ganglion cell complex and retinal nerve fiber layer scans in Fourier-domain optical coherence tomography","volume":"4","author":"Zhang","year":"2015","journal-title":"Transl Vis Sci Technol"},{"key":"bib21","first-page":"6105","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","volume":"97","author":"Tan","year":"2019","journal-title":"ICML"},{"issue":"2","key":"bib24","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/0002-9394(89)90006-8","article-title":"Test\u2013retest variability in glaucomatous visual fields","volume":"108","author":"Heijl","year":"1989","journal-title":"Am J Ophthalmol"},{"issue":"7","key":"bib25","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1016\/j.ophtha.2014.01.020","article-title":"Assessment of the reliability of standard automated perimetry in regions of glaucomatous damage","volume":"121","author":"Gardiner","year":"2014","journal-title":"Ophthalmology"},{"issue":"4","key":"bib26","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.ogla.2020.12.001","article-title":"Predicting global test\u2013retest variability of visual fields in glaucoma","volume":"4","author":"Choi","year":"2021","journal-title":"Ophthalmol Glaucoma"},{"issue":"12","key":"bib27","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1001\/archopht.118.12.1639","article-title":"The advanced glaucoma intervention study, 6: effect of cataract on visual field and visual acuity","volume":"118","author":"The AGIS Investigators","year":"2000","journal-title":"Arch Ophthalmol"},{"key":"bib16","first-page":"783","article-title":"Estimating glaucomatous visual sensitivity from retinal thickness with pattern-based regularization and visualization","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Sugiura","year":"2018"},{"key":"bib22","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1109\/ICCVW.2019.00240","article-title":"Resource efficient 3D convolutional neural networks","volume-title":"2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW)","author":"Kopuklu","year":"2019"},{"key":"bib23","article-title":"EfficientNet-PyTorch-3D","author":"Shi"}],"container-title":["Translational Vision Science &amp; Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?doi=10.1167\/tvst.15.7.17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:00:50Z","timestamp":1783944050000},"score":1,"resource":{"primary":{"URL":"http:\/\/tvst.arvojournals.org\/article.aspx?doi=10.1167\/tvst.15.7.17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":27,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7,1]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.15.7.17","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,13]]}}}