{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T12:13:23Z","timestamp":1768652003177,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T00:00:00Z","timestamp":1709337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["LA\/P\/0063\/2020"],"award-info":[{"award-number":["LA\/P\/0063\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients\u2019 responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts\u2019 decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.<\/jats:p>","DOI":"10.3390\/medicina60030428","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T08:03:47Z","timestamp":1709539427000},"page":"428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening"],"prefix":"10.3390","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2207-0897","authenticated-orcid":false,"given":"Jos\u00e9","family":"Camara","sequence":"first","affiliation":[{"name":"Engineering Department, Universidade Tras-os-Montes (UTAD), 5000801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3458-7693","authenticated-orcid":false,"given":"Antonio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Engineering Department, Universidade Tras-os-Montes (UTAD), 5000801 Vila Real, Portugal"},{"name":"INESCTEC\u2014INESC Technology and Science, 4200465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,2]]},"reference":[{"key":"ref_1","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":"ref_2","unstructured":"World Health Organization (2019). World Report on Vision, World Health Organization. Available online: https:\/\/iris.who.int\/handle\/10665\/328717."},{"key":"ref_3","first-page":"12","article-title":"Glaucoma: Seguimento cl\u00ednico e exames complementares","volume":"1","author":"Esporcatte","year":"2022","journal-title":"Glaucoma Seguimento Cl\u00edn. E Exames Complement."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mariottoni, E.B., Jammal, A.A., Berchuck, S.I., Tavares, I.M., and Medeiros, F.A. (2020). An Objective Structural and Functional Reference Standard for Diagnostic Studies in Glaucoma. Ophthalmology, 2020-04.","DOI":"10.1101\/2020.04.10.20057836"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Camara, J., Neto, A., Pires, I.M., Villasana, M.V., Zdravevski, E., and Cunha, A. (2022). Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. J. Imaging, 8.","DOI":"10.3390\/jimaging8020019"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.1001\/jamaophthalmol.2019.3501","article-title":"Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs","volume":"137","author":"Liu","year":"2019","journal-title":"JAMA Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e157968","DOI":"10.1172\/JCI157968","article-title":"A deep-learning system predicts glaucoma incidence and progression using retinal photographs","volume":"132","author":"Li","year":"2022","journal-title":"J. Clin. Investig."},{"key":"ref_8","first-page":"275","article-title":"Detection of Glaucoma Using Image Processing Techniques: A Critique","volume":"33","author":"Kumar","year":"2018","journal-title":"Semin. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1159\/000500980","article-title":"Comparison of fundus biomicroscopy examination of the optic nerve head with and without mydriasis","volume":"63","author":"Colicchio","year":"2020","journal-title":"Ophthalmic Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1097\/IJG.0000000000000640","article-title":"The Association Between Clinical Features Seen on Fundus Photographs and Glaucomatous Damage Detected on Visual Fields and Optical Coherence Tomography Scans","volume":"26","author":"Alhadeff","year":"2017","journal-title":"J. Glaucoma"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.ophtha.2011.03.019","article-title":"Assessment of visual function in glaucoma: A report by the American Academy of Ophthalmology","volume":"118","author":"Jampel","year":"2011","journal-title":"Ophthalmology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.2147\/OPTH.S340508","article-title":"Spotlight on iPad Visual Field Tests Efficacy","volume":"16","author":"Ichhpujani","year":"2022","journal-title":"Clin. Ophthalmol. Auckl. NZ"},{"key":"ref_13","unstructured":"Diniz Filho, A., and Schimiti, R.B. (2022). Avalia\u00e7\u00e3o do Campo Visual No Glaucoma. Soc. Bras. Glaucoma, Available online: http:\/\/\/www.subglaucoma.org.br\/medico\/wp-content\/uploads\/2023\/12\/03-Diretriz-Avaliacao-Do-Campo-Visual.pdf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1111\/aos.13290","article-title":"Detection of glaucoma progression by perimetry and optic disc photography at different stages of the disease: Results from the Early Manifest Glaucoma Trial","volume":"95","author":"Heijl","year":"2017","journal-title":"Acta Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.ophtha.2022.02.017","article-title":"Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging","volume":"129","author":"Kihara","year":"2022","journal-title":"Ophthalmology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wen, J.C., Lee, C.S., Keane, P.A., Xiao, S., Rokem, A.S., Chen, P.P., Wu, Y., and Lee, A.Y. (2019). Forecasting future Humphrey Visual Fields using deep learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214875"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"267","DOI":"10.4274\/tjo.86461","article-title":"Are all retinal nerve fiber layer defects on optic coherence tomography glaucomatous?","volume":"47","author":"Ahmet","year":"2017","journal-title":"Turk. J. Ophthalmol."},{"key":"ref_18","first-page":"2243","article-title":"Rates of Retinal Nerve Fiber Layer Loss in the Contralateral Eyes of Glaucoma Patients Showing Unilateral Progression by Conventional Methods","volume":"122","author":"Liu","year":"2015","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1586\/17434440.2013.827505","article-title":"Improved visualization of deep ocular structures in glaucoma using high penetration optical coherence tomography","volume":"10","author":"Mansouri","year":"2013","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1167\/tvst.9.2.42","article-title":"A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression","volume":"9","author":"Thompson","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"149","DOI":"10.18240\/ijo.2020.01.22","article-title":"Current applications of machine learning in the screening and diagnosis of glaucoma: A systematic review and meta-analysis","volume":"13","author":"Murtagh","year":"2020","journal-title":"Int. J. Ophthalmol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41433-020-01191-5","article-title":"Deep learning in glaucoma with optical coherence tomography: A review","volume":"35","author":"Ran","year":"2021","journal-title":"Eye"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"27737","DOI":"10.1007\/s11042-022-12826-y","article-title":"Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images","volume":"81","author":"Singh","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"100900","DOI":"10.1016\/j.preteyeres.2020.100900","article-title":"Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective","volume":"82","author":"Li","year":"2021","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1016\/j.ophtha.2017.05.026","article-title":"The current state of teleophthalmology in the United States","volume":"124","author":"Rathi","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104089","DOI":"10.1016\/j.ijmedinf.2020.104089","article-title":"Teleophthalmology for the elderly population: A review of the literature","volume":"136","author":"Fatehi","year":"2020","journal-title":"Int. J. Med. Inf."},{"key":"ref_28","first-page":"3","article-title":"TeleOftalmo: Strategy to expand the offer of ophthalmologic telediagnostics for primary healthcare in the Southern Brazil","volume":"38","author":"Moreira","year":"2022","journal-title":"Cad. Sa\u00fade P\u00fablica"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lutz de Araujo, A., Moreira, T.d.C., Varvaki Rados, D.R., Gross, P.B., Molina-Bastos, C.G., Katz, N., Hauser, L., Souza da Silva, R., Gadenz, S.D., and Dal Moro, R.G. (2020). The use of telemedicine to support Brazilian primary care physicians in managing eye conditions: The TeleOftalmo Project. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231034"},{"key":"ref_30","first-page":"1909","article-title":"Smartphones, tele-ophthalmology, and VISION 2020","volume":"10","author":"Mohammadpour","year":"2017","journal-title":"Int. J. Ophthalmol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1136\/bjophthalmol-2017-311452","article-title":"Review of economic evaluations of teleophthalmology as a screening strategy for chronic eye disease in adults","volume":"102","author":"Sharafeldin","year":"2018","journal-title":"Br. J. Ophthalmol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1097\/APO.0000000000000416","article-title":"Real-Time Mobile Teleophthalmology for the Detection of Eye Disease in Minorities and Low Socioeconomics At-Risk Populations","volume":"10","author":"Elgin","year":"2021","journal-title":"Asia-Pac. J. Ophthalmol."},{"key":"ref_34","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_35","unstructured":"Chollet, F. (2018). Deep Learning with Python, Manning Publications Co."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e94","DOI":"10.1111\/aos.14193","article-title":"Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning","volume":"98","author":"Hemelings","year":"2020","journal-title":"Acta Ophthalmol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1097\/ICU.0000000000000649","article-title":"Glaucoma screening: Where are we and where do we need to go?","volume":"31","author":"Tan","year":"2020","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.07.012","article-title":"Computer-aided diagnosis of glaucoma using fundus images: A review","volume":"165","author":"Hagiwara","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_39","first-page":"728","article-title":"Screening and management of retinal diseases using digital medicine","volume":"115","author":"Gerendas","year":"2018","journal-title":"Ophthalmol. Z. Dtsch. Ophthalmol. Ges."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jeong, Y., Hong, Y.-J., and Han, J.-H. (2022). Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010134"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1167\/tvst.11.1.37","article-title":"Glaucoma Suspects: The Impact of Risk Factor-Driven Review Periods on Clinical Load, Diagnoses, and Healthcare Costs","volume":"11","author":"Phu","year":"2022","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"193","DOI":"10.4274\/tjo.galenos.2021.29726","article-title":"Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography","volume":"52","author":"Atalay","year":"2022","journal-title":"Turk. J. Ophthalmol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"E52","DOI":"10.5888\/pcd18.200567","article-title":"A Randomized Trial to Improve Adherence to Follow-up Eye Examinations among People with Glaucoma","volume":"18","author":"Leiby","year":"2021","journal-title":"Prev. Chronic Dis."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s11517-020-02237-2","article-title":"Automated glaucoma screening method based on image segmentation and feature extraction","volume":"58","author":"Guo","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1260-2","article-title":"Automated framework for screening of glaucoma through cloud computing","volume":"43","author":"Soorya","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.procs.2021.12.040","article-title":"Optic disc and cup segmentations for glaucoma assessment using cup-to-disc ratio","volume":"196","author":"Neto","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Camara, J., Rezende, R., Pires, I.M., and Cunha, A. (2022). Retinal Glaucoma Public Datasets: What Do We Have and What Is Missing?. J. Clin. Med., 11.","DOI":"10.3390\/jcm11133850"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shuldiner, S.R., Boland, M.V., Ramulu, P.Y., De Moraes, C.G., Elze, T., Myers, J., Pasquale, L., Wellik, S., and Yohannan, J. (2021). Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0249856"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kucur, \u015e.S., Holl\u00f3, G., and Sznitman, R. (2018). A deep learning approach to automatic detection of early glaucoma from visual fields. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0206081"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"832920","DOI":"10.3389\/fmed.2022.832920","article-title":"A Structure-Related Fine-Grained Deep Learning System With Diversity Data for Universal Glaucoma Visual Field Grading","volume":"9","author":"Huang","year":"2022","journal-title":"Front. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100222","DOI":"10.1016\/j.xops.2022.100222","article-title":"Visual Field Prediction","volume":"3","author":"Eslami","year":"2023","journal-title":"Ophthalmol. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lim, W.S., Ho, H.-Y., Ho, H.-C., Chen, Y.-W., Lee, C.-K., Chen, P.-J., Lai, F., Jang, J.-S.R., and Ko, M.-L. (2022). Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: Focus group study on high prevalence of myopia. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00933-z"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Neto, A., Camara, J., and Cunha, A. (2022). Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device. Sensors, 22.","DOI":"10.3390\/s22041449"},{"key":"ref_54","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."}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/60\/3\/428\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:28Z","timestamp":1760105308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/60\/3\/428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,2]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["medicina60030428"],"URL":"https:\/\/doi.org\/10.3390\/medicina60030428","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,2]]}}}