Bennett, C. F. et al. (2024) “Associations between quantitative contrast sensitivity, optical coherence tomography and progression prognostication in intermediate age-related macular degeneration,” Investigative Ophthalmology & Visual Science, 65.
Vingopoulos, F. et al. (2024) “Quantitative Contrast Sensitivity Longitudinal Changes Correlate Better than Visual Acuity with Geographic Atrophy Size Progression in Advanced Dry AMD: a New Clinical Endpoint,” Investigative Ophthalmology & Visual Science, 65.
Structure-function associations between OCT features and quantitative contrast sensitivity function in non-proliferative diabetic retinopathy (no date) Euretina.org. Available at: https://abstracts.euretina.org/2024/ca24-2625-8434/r/reczXFkDyN0qRzQaj.
Romano, F. et al. (2024) “Decreased macular choriocapillaris perfusion correlates with contrast sensitivity function in dry age-related macular degeneration,” Ophthalmology retina. doi: 10.1016/j.oret.2024.06.005.
Ophthalmology Clinical Data Science Research Group: Data Availability and Missingness in the IRIS® Database
Ophthalmology Clinical Data Science Research Group: Data Availability and Missingness in the IRIS® Database
Ophthalmology Clinical Data Science Research Group: Characterization of Acute Retinal Necrosis and Subsequent Retinal Detachment Risk: An IRIS® Registry Analysis
Ophthalmology Clinical Data Science Research Group: Genetics of Diabetic Retinopathy
Ophthalmology Clinical Data Science Research Group: Genetics of Diabetic Retinopathy
Sobrin, L. et al. (2022) “Gene set enrichment analsyes identify pathways involved in genetic risk for diabetic retinopathy,” American journal of ophthalmology, 233, pp. 111–123. doi: 10.1016/j.ajo.2021.06.014.
Pollack, S. et al. (2019) “Multiethnic genome-wide association study of diabetic retinopathy using liability threshold modeling of duration of diabetes and glycemic control,” Diabetes, 68(2), pp. 441–456. doi: 10.2337/db18-0567.
Sobrin, L. et al. (2017) “Genetically determined plasma lipid levels and risk of diabetic retinopathy: A Mendelian randomization study,” Diabetes, 66(12), pp. 3130–3141. doi: 10.2337/db17-0398.
Penman, A. et al. (2015) “P-selectin plasma levels and genetic variant associated with diabetic retinopathy in African Americans,” American journal of ophthalmology, 159(6), pp. 1152-1160.e2. doi: 10.1016/j.ajo.2015.03.008.
Ophthalmology Clinical Data Science Research Group: Epidemiology of Diabetic Retinopathy
Susarla, G. et al. (2022) “Younger age and albuminuria are associated with proliferative DiAbeTic Retinopathy and diabetic macular edema in the South Indian GeNetics of DiAbeTic Retinopathy (SIGNATR) Study,” Current eye research, 47(10), pp. 1389–1396. doi: 10.1080/02713683.2022.2091148.
Davoudi, S. et al. (2016) “Optical coherence tomography characteristics of macular edema and hard exudates and their association with lipid serum levels in Type 2 diabetes,” Retina (Philadelphia, Pa.), 36(9), pp. 1622–1629. doi: 10.1097/IAE.0000000000001022.
Penman, A. et al. (2016) “Risk factors for proliferative diabetic retinopathy in African Americans with type 2 diabetes,” Ophthalmic epidemiology, 23(2), pp. 88–93. doi: 10.3109/09286586.2015.1119287.
Lee, W. J. et al. (2014) “The relationship between diabetic retinopathy and diabetic nephropathy in a population-based study in Korea (KNHANES V-2, 3),” Investigative ophthalmology & visual science, 55(10), pp. 6547–6553. doi: 10.1167/iovs.14-15001.
Ophthalmology Clinical Data Science Research Group: Armstrong Research
Ophthalmology Clinical Data Science Research Group: Rossin Research
Rämö, J. T. et al. (2024) “Rare genetic variation in VE-PTP is associated with central serous chorioretinopathy, venous dysfunction and glaucoma,” medRxiv. doi: 10.1101/2024.05.08.24307013.
Rämö, J. T., Kim, L. A., et al. (2024) “Targeting the Tie-2 receptor with faricimab in central serous chorioretinopathy: A case series motivated by a genetic finding,” American journal of ophthalmology, 269, pp. 246–254. doi: 10.1016/j.ajo.2024.08.040.
Hauser, B. M. et al. (2024) “Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease,” npj genomic medicine, 9(1). doi: 10.1038/s41525-024-00416-w.
Shah, P. P. et al. (2023) “Open globe injuries from garage door springs,” Ophthalmic surgery, lasers & imaging retina, 54(11), pp. 666–669. doi: 10.3928/23258160-20231002-01.
Rämö, J. T. et al. (2023) “Overlap of genetic loci for central serous chorioretinopathy with age-related macular degeneration,” JAMA ophthalmology, 141(5), p. 449. doi: 10.1001/jamaophthalmol.2023.0706.
Tieger, M. et al. (2022) “SARS-CoV-2 RNA detected in vitreous samples obtained at autopsy,” Journal of vitreoretinal diseases, 6(3), pp. 183–187. doi: 10.1177/24741264221083408.
Nathan, A. et al. (2021) “Structure-guided T cell vaccine design for SARS-CoV-2 variants and sarbecoviruses,” Cell, 184(17), pp. 4401-4413.e10. doi: 10.1016/j.cell.2021.06.029.
Rossin, E. J. et al. (2021) “Traumatic Retinal Detachment in Patients with Self-Injurious Behavior: An International Multicenter Study, Ophthalmol Retina,” Ophthalmol Retina. doi: 10.1016/j.oret.2020.11.012.
Rossin, E. J. et al. (2021) “Factors associated with increased risk of serious ocular injury in the setting of orbital fracture,” JAMA ophthalmology, 139(1), pp. 77–83. doi: 10.1001/jamaophthalmol.2020.5108.
Gaiha, G. D. et al. (2019) “Structural topology defines protective CD8+ T cell epitopes in the HIV proteome,” Science (New York, N.Y.), 364(6439), pp. 480–484. doi: 10.1126/science.aav5095.
Ophthalmology Clinical Data Science Research Group Contributions
Ciociola, E. C. et al. (2024) “Racial disparities in glaucoma vision outcomes and eye care utilization: An IRIS® Registry analysis,” American journal of ophthalmology, 264, pp. 194–204. doi: 10.1016/j.ajo.2024.03.022.
Gong, D. et al. (2024) “Fellow eyes conversion rates in patients with unilateral exudative age-related macular degeneration: An Academy IRIS® Registry analysis,” Ophthalmic surgery, lasers & imaging retina, 55(4), pp. 220–226. doi: 10.3928/23258160-20240125-01.
Lu, J. E. et al. (2024) “Epidemiology of orbital inflammatory disease: An AAO IRIS® registry study,” Ocular immunology and inflammation, pp. 1–4. doi: 10.1080/09273948.2024.2322013.
Singh, R. B. et al. (2024) “Corneal opacity in the United States: An American academy of ophthalmology IRIS® Registry (Intelligent Research in Sight) study,” Ophthalmology. doi: 10.1016/j.ophtha.2024.07.005.
Zhang, L. J. et al. (2024) “Visual outcomes of children undergoing penetrating keratoplasty in the US,” The ocular surface, 32, pp. 219–221. doi: 10.1016/j.jtos.2024.02.001.
Ophthalmology Clinical Data Science Research Group: Risk Factors for Uveitis from Large Health Care Claim Databases
Butler, N. J. et al. (2023) “Dual-energy X-ray absorptiometry scan utilization and skeletal fragility among non-infectious uveitis patients exposed to oral glucocorticoids,” Ocular immunology and inflammation, pp. 1–9. doi: 10.1080/09273948.2023.2182793.
Sobrin, L., Yu, Y., Li, A., et al. (2022) “Angiotensin converting enzyme-inhibitors and incidence of non-infectious uveitis in a large healthcare claims database,” Ophthalmic epidemiology, 29(1), pp. 25–30. doi: 10.1080/09286586.2021.1887284.
Sobrin, L., Yu, Y., Han, S., et al. (2022) “Risk of non-infectious uveitis with metformin therapy in a large healthcare claims database,” Ocular immunology and inflammation, 30(6), pp. 1334–1340. doi: 10.1080/09273948.2021.1872650.
Susarla, G. et al. (2022) “Mendelian randomization shows a causal effect of low Vitamin D on non-infectious uveitis and scleritis risk,” American journal of ophthalmology, 244, pp. 11–18. doi: 10.1016/j.ajo.2022.08.001.
Sobrin, L. et al. (2021) “Decreased risk of non-infectious anterior uveitis with statin therapy in a large healthcare claims database,” Graefe s Archive for Clinical and Experimental Ophthalmology, 259(9), pp. 2783–2793. doi: 10.1007/s00417-021-05243-8.
Sobrin, L. et al. (2020) “Risk of noninfectious uveitis with female hormonal therapy in a large healthcare claims database,” Ophthalmology, 127(11), pp. 1558–1566. doi: 10.1016/j.ophtha.2020.04.034.
Sobrin, L. et al. (2018) “Association of hypovitaminosis D with increased risk of uveitis in a large health care claims database,” JAMA ophthalmology, 136(5), pp. 548–552. doi: 10.1001/jamaophthalmol.2018.0642.
Grotting, L. A. et al. (2017) “Association of low vitamin D levels with noninfectious anterior uveitis,” JAMA ophthalmology, 135(2), pp. 150–153. doi: 10.1001/jamaophthalmol.2016.4888.
Ophthalmology Clinical Data Science Research Group: Friedman Research
Tran, J. H. et al. (2024) “Use of diagnostic codes for primary open-angle glaucoma polygenic risk score construction in electronic health record-linked biobanks,” American journal of ophthalmology, 267, pp. 204–212. doi: 10.1016/j.ajo.2024.06.007.
Medeiros, F. A. et al. (2024) “Short-term detection of fast progressors in glaucoma: The Fast Progression Assessment through Clustered Evaluation (fast-PACE) study,” Ophthalmology, 131(6), pp. 645–657. doi: 10.1016/j.ophtha.2023.12.031.
Halawa, O. A. et al. (2023) “Factors associated with glaucoma-specific quality of life in a US glaucoma clinic in a pilot implementation of an online computerised adaptive test (GlauCAT),” The British journal of ophthalmology, 107(8), pp. 1079–1085. doi: 10.1136/bjophthalmol-2022-321145.
Freeman, S. E. et al. (2023) “Participant experience using novel perimetry tests to monitor glaucoma progression,” Journal of glaucoma, 32(11), pp. 948–953. doi: 10.1097/IJG.0000000000002296.
Sekimitsu, S. et al. (2023) “Deep ocular phenotyping across primary open-angle glaucoma genetic burden,” JAMA ophthalmology, 141(9), pp. 891–899. doi: 10.1001/jamaophthalmol.2023.3645.
Kang, J. et al. (2023) “Comparison of perimetric outcomes from a tablet perimeter, smart visual function analyzer, and Humphrey Field Analyzer,” Ophthalmology. Glaucoma, 6(5), pp. 509–520. doi: 10.1016/j.ogla.2023.03.001.
Hark, L. A. et al. (2023) “Manhattan Vision Screening and Follow-up study (NYC-SIGHT): Baseline results and costs of a cluster-randomized trial,” American journal of ophthalmology, 251, pp. 12–23. doi: 10.1016/j.ajo.2023.01.019.
Yuan, Y. et al. (2023) “Fourteen-year outcome of angle-closure prevention with laser iridotomy in the Zhongshan Angle-Closure Prevention Study: Extended follow-up of a randomized controlled trial,” Ophthalmology, 130(8), pp. 786–794. doi: 10.1016/j.ophtha.2023.03.024.
Mitchell, W. G. et al. (2023) “Predictors of long-term intraocular pressure control after lens extraction in primary angle closure glaucoma: results from the EAGLE trial,” The British journal of ophthalmology, 107(8), pp. 1072–1078. doi: 10.1136/bjophthalmol-2021-319765.
Ramke, J. et al. (2022) “Grand Challenges in global eye health: a global prioritisation process using Delphi method,” The Lancet. Healthy longevity, 3(1), pp. e31–e41. doi: 10.1016/s2666-7568(21)00302-0.
Halawa, O. A. et al. (2021) “Population-based utility of van Herick grading for angle-closure detection,” Ophthalmology, 128(12), pp. 1779–1782. doi: 10.1016/j.ophtha.2021.06.010.
Harvard Ophthalmology AI Lab: Dr. Zebardast Representative Project Publications
Harvard Ophthalmology AI Lab: Dr. Zebardast Representative Project Publications
Kazeminasab Hashemabad, S. et al. (2024) “Genetically Adjusted Optic Cup to Disc Ratio (CDR) Using a Two-Phase Training Deep Learning Model,” Investigative Ophthalmology & Visual Science, 65.
Zebardast, N. et al. (2023) “Genome wide discovery via feature space mapping of deep-learning derived clinical OCT phenotypes to the UK biobank,” Investigative Ophthalmology & Visual Science, 64.
Kazeminasab, S. et al. (2023) “An artificial intelligence method for phenotyping of OCT scans using unsupervised and self-supervised deep learning,” bioRxiv. doi: 10.1101/2023.10.20.563205.
Moradi, M. et al. (2024) “PyGlaucoMetrics: An Open-Source Multi-Criteria Glaucoma Defect Evaluation,” Investigative Ophthalmology & Visual Science, 65.
Kazeminasab Hashemabad, S. et al. (2024) “An unsupervised deep learning method for identifying glaucoma progression patterns using longitudinal ganglion cell complex (GCC) scans,” Investigative Ophthalmology & Visual Science, 65.
Kazeminasab Hashemabad, S. et al. (2023) “A graph neural network-based clustering method for glaucoma detection from OCT scans considering uncertainties in the number of clusters,” Investigative Ophthalmology & Visual Science, 64.
Kazeminasab Hashemabad, S. et al. (2023) “A Performance Evaluation Method for Unsupervised OCT Phenotype Discovery using Deep Learning,” Investigative Ophthalmology & Visual Science, 64.
Harvard Ophthalmology AI Lab: Equitable AI for disease screening
Harvard Ophthalmology AI Lab: Equitable AI for disease screening
Luo, Y. et al. (2024) “Harvard Glaucoma Fairness: A retinal nerve disease dataset for fairness learning and fair identity normalization,” IEEE transactions on medical imaging, 43(7), pp. 2623–2633. doi: 10.1109/TMI.2024.3377552.
Harvard Ophthalmology AI Lab: AI for identifying imaging endophenotypes for GWAS
Zebardast, N. et al. (2022) “Deep unsupervised discovery of OCT phenotypes enables genome-wide analyses,” Investigative Ophthalmology & Visual Science, 63(7), pp. 1844–1844.
Harvard Ophthalmology AI Lab: Vision loss detection and progression prediction
Choi, E. Y. et al. (2021) “Predicting global test-retest variability of visual fields in glaucoma,” Ophthalmology. Glaucoma, 4(4), pp. 390–399. doi: 10.1016/j.ogla.2020.12.001.
Teng, B. et al. (2020) “Inter-eye association of visual field defects in glaucoma and its clinical utility,” Translational vision science & technology, 9(12), p. 22. doi: 10.1167/tvst.9.12.22.
Teng, B. et al. (2020) “Inter-eye association of visual field defects in glaucoma and its clinical utility,” Translational vision science & technology, 9(12), p. 22. doi: 10.1167/tvst.9.12.22.
Wang, M., Tichelaar, J., et al. (2020) “Characterization of central visual field loss in end-stage glaucoma by unsupervised artificial intelligence,” JAMA ophthalmology, 138(2), pp. 190–198. doi: 10.1001/jamaophthalmol.2019.5413.
Wang, M. et al. (2019) “An artificial intelligence approach to detect visual field progression in glaucoma based on spatial pattern analysis,” Investigative ophthalmology & visual science, 60(1), pp. 365–375. doi: 10.1167/iovs.18-25568.
Wang, M. et al. (2018) “Reversal of glaucoma hemifield test results and visual field features in glaucoma,” Ophthalmology, 125(3), pp. 352–360. doi: 10.1016/j.ophtha.2017.09.021.
Harvard Ophthalmology AI Lab: Epidemiology and demographic features related to eye diseases
Ciociola, E. C. et al. (2023) “Effectiveness of trabeculectomy and tube shunt with versus without concurrent phacoemulsification: Intelligent Research in Sight Registry longitudinal analysis,” Ophthalmology. Glaucoma, 6(1), pp. 42–53. doi: 10.1016/j.ogla.2022.07.003.
Yang, S.-A. et al. (2023) “Effectiveness of microinvasive glaucoma surgery in the United States: Intelligent Research in Sight Registry analysis 2013-2019,” Ophthalmology, 130(3), pp. 242–255. doi: 10.1016/j.ophtha.2022.10.021.
Baniasadi, N. et al. (2020) “Norms of interocular circumpapillary retinal nerve fiber layer thickness differences at 768 retinal locations,” Translational vision science & technology, 9(9), p. 23. doi: 10.1167/tvst.9.9.23.
Li, D. et al. (2020) “Sex-specific differences in circumpapillary retinal nerve fiber layer thickness,” Ophthalmology, 127(3), pp. 357–368. doi: 10.1016/j.ophtha.2019.09.019.
Harvard Ophthalmology AI Lab: Responsible AI
Eslami, M. et al. (2024) “A Practical Barrier: AI-Powered CDR Extraction in Fundus Photos,” Investigative Ophthalmology & Visual Science, 65.
Kalahasty, R. et al. (2023) “Evaluation of Landmark Localization Models for Fundus Imaging Conditions,” Investigative Ophthalmology & Visual Science, 64.
Motati, L. S. et al. (2023) “Evaluation of Robustness of Disc/Cup Segmentation in Different Fundus Imaging Conditions,” Investigative Ophthalmology & Visual Science, 64.
Eslami, M. et al. (2023) “Deep Learning based Adversarial Disturbances in Fundus Image Analysis,” Investigative Ophthalmology & Visual Science, 64.
Eslami, Mohammad et al. (2023) “Visual field prediction,” Ophthalmology science, 3(1), p. 100222. doi: 10.1016/j.xops.2022.100222.
Eslami, M. et al. (2022) “Evaluation of Deep Learning Visual Field Prediction Models for Clinical Relevance,” Investigative Ophthalmology & Visual Science, 63, pp. 2012-A0453.
Harvard Ophthalmology AI Lab: Visual Field Toolbox
Moradi, M. et al. (2024) “PyGlaucoMetrics: An Open-Source Multi-Criteria Glaucoma Defect Evaluation,” Investigative Ophthalmology & Visual Science, 65.
Eslami, M. et al. (2023) “PyVisualFields: A python package for visual field analysis,” Translational vision science & technology, 12(2), p. 6. doi: 10.1167/tvst.12.2.6.
Kazeminasab, S. et al. (2022) “A Python collection of tools for analyzing visual fields,” Investigative Ophthalmology & Visual Science, 63, pp. 2009-A0450.
Harvard Ophthalmology AI Lab: AI for medical data cleaning
Shi, M. et al. (2024) “RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps,” Medical image analysis, 94(103110), p. 103110. doi: 10.1016/j.media.2024.103110.
Shi, M. et al. (2023) “Artifact-tolerant clustering-guided contrastive embedding learning for ophthalmic images in glaucoma,” IEEE journal of biomedical and health informatics, 27(9), pp. 4329–4340. doi: 10.1109/JBHI.2023.3288830.
Harvard Ophthalmology AI Lab: Interpretable AI for pathophysiology discovery
Luo, Y. et al. (2023) “Assessing Retinal Layers’ Association with Diabetes using a Deep Learning Framework,” Investigative Ophthalmology & Visual Science, 64(9), pp. B0033-B33.
Tian, Y. et al. (2023) “The Impact of Age-Related Macular Degeneration on Retinal Layers Quantified by Deep Learning,” Investigative Ophthalmology & Visual Science, 64(9), pp. P0014-P14.
Harvard Ophthalmology AI Lab: Relationship between retinal structure and visual function in eye diseases
Saini, C. et al. (2022) “Assessing surface shapes of the optic nerve head and peripapillary retinal nerve fiber layer in glaucoma with artificial intelligence,” Ophthalmology science, 2(3), p. 100161. doi: 10.1016/j.xops.2022.100161.
Wang, M. et al. (2020) “An artificial intelligence approach to assess spatial patterns of retinal nerve fiber layer thickness maps in glaucoma,” Translational vision science & technology, 9(9), p. 41. doi: 10.1167/tvst.9.9.41.
Wang, M. et al. (2018) “The interrelationship between refractive error, blood vessel anatomy, and glaucomatous visual field loss,” Translational vision science & technology, 7(1), p. 4. doi: 10.1167/tvst.7.1.4.
Wang, M. et al. (2017) “Relationship between central retinal vessel trunk location and visual field loss in glaucoma,” American journal of ophthalmology, 176, pp. 53–60. doi: 10.1016/j.ajo.2017.01.001.
Baniasadi, N. et al. (2017) “Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters,” Journal of biomedical optics, 22(12), pp. 1–9. doi: 10.1117/1.JBO.22.12.121714.
Elze, T. et al. (2017) “Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle,” Journal of biomedical optics, 22(12), pp. 1–11. doi: 10.1117/1.JBO.22.12.121713.
Harvard Ophthalmology AI Lab: Aging and lifestyle affecting the eye
Girbardt, J. et al. (2021) “Reading cognition from the eyes: association of retinal nerve fibre layer thickness with cognitive performance in a population-based study,” Brain communications, 3(4), p. fcab258. doi: 10.1093/braincomms/fcab258.
Rauscher, F. G. et al. (2021) “Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study,” BMC medicine, 19(1), p. 202. doi: 10.1186/s12916-021-02064-8.
Harvard Ophthalmology AI Lab: Early Identification for Pre-onset High-Risk Glaucoma Patients
Eslami, M. et al. (2024) “Feasibility of Identifying High-Risk Glaucoma Patients Before the Onset of Disease,” Investigative Ophthalmology & Visual Science, 65.
Sekimitsu, S. et al. (2024) “Genetic risk for open angle glaucoma subtypes is associated with specific visual field defect classes,” Investigative Ophthalmology & Visual Science, 65.
Eslami, M. et al. (2023) “Visual Field (VF) change-based archetype analysis for early-stage glaucoma detection,” Investigative Ophthalmology & Visual Science, 64.
Rezaei, M. et al. (2023) “Self-supervised Learning and Self-labeling Framework for Retina Glaucoma Detection,” Investigative Ophthalmology & Visual Science, 64.
Luo, Y. et al. (2023) “Retinal Surface Contour is Predictive of Fast Glaucoma Progression with Deep Learning,” Investigative Ophthalmology & Visual Science, 64.
Aziz, K. et al. (2023) “Genome-wide Polygenic Risk Score for Primary Open Angle Glaucoma is Associated with More Severe Disease in a Multi-Ethnic Biobank,” Investigative Ophthalmology & Visual Science, 64.
Halawa, O. A. et al. (2022) “Race and ethnicity differences in disease severity and visual field progression among glaucoma patients,” American journal of ophthalmology, 242, pp. 69–76. doi: 10.1016/j.ajo.2022.05.023.
Harvard Ophthalmology AI Lab: Joining Dr. Lains, previously at Harvard Retinal Imaging Lab
Lains, I. et al. (2024) “Treatment Regimen and Outcomes in Acute Retinal Necrosis – an IRIS® Registry Study,” Ophthalmology.
Mendez, K. et al. (2024) “Metabolomic-derived endotypes of age-related macular degeneration (AMD): a step towards identification of disease subgroups,” Scientific reports, 14(1), p. 12145. doi: 10.1038/s41598-024-59045-z.
Lains, I. et al. (2024) “Plasma metabolites associated with OCT features of age-related macular degeneration,” Ophthalmology science, 4(1), p. 100357. doi: 10.1016/j.xops.2023.100357.
Han, X. et al. (2023) “Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration,” Cell reports. Medicine, 4(7), p. 101085. doi: 10.1016/j.xcrm.2023.101085.
Lains, I. et al. (2021) “Genomic-Metabolomic Associations Support Role of LIPC gene and Glycerophospholipids in Age-Related Macular Degeneration,” Ophthalmology Science.
Lains, I. et al. (2021) “Baseline Predictors Associated with Three-Year Changes in Dark Adaptation in Age-related Macular Degeneration,” Retina.
Lains, I. et al. (2016) “Second primary neoplasms in patients with uveal melanoma: a population-based study,” American Journal of Ophthalmology.