About My Research
Center/Research Area Affiliations
Biography
Dr. Elze is a computational vision scientist at Schepens Eye Research Institute of Mass Eye and Ear. His research addresses the methodology of basic and clinical vision science, such as optimal design of experiments and clinical studies and optimal data analysis (particularly for large and high-dimensional datasets). Dr. Elze's current projects include the functional characterization of eye diseases like glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy, and the relationship between retinal structure and functional vision loss. Dr. Elze also develops adaptive sampling techniques for efficient clinical function testing and investigates display technology for clinical applications.
As a former member of the research group “Complex Structures in Biology and Cognition” at Max Planck Institute for Mathematics in the Sciences, Dr. Elze focused on methodology in visual neuroscience and psychophysics. In 2011, he joined the laboratory of Dr. Peter Bex at Schepens Eye Research Institute of Mass Eye and Ear, where he started to investigate ophthalmic diseases using psychophysical and bioinformatical methods and machine learning. In 2013, he became an Instructor in Ophthalmology and in 2017 an Assistant Professor of Ophthalmology at Harvard Medical School. His group works in the intersection among mathematics, computer science, and clinical ophthalmology.
Education
2011: PhD, Computer Science, Max Planck Institute for Mathematics in the Sciences
Postgraduate Training
2001-2013: Postdoctoral Fellow, Schepens Eye Research Institute of Mass. Eye and Ear
Honors
2013: Best Clinical Poster, Harvard Ophthalmology Annual Meeting and Alumni Reunion
- Glucose tolerance and insulin resistance/sensitivity associate with retinal layer characteristics: the LIFE-Adult-Study. Diabetologia. 2024 Mar 02.
- RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps. Med Image Anal. 2024 Feb 29; 94:103110.
- Visual outcomes of children undergoing penetrating keratoplasty in the US. Ocul Surf. 2024 Feb 23.
- The Incidence of Strabismus After Upper and Lower Blepharoplasty in the United States. Ophthalmic Plast Reconstr Surg. 2024 Feb 09.
- Fellow Eyes Conversion Rates in Patients With Unilateral Exudative Age-Related Macular Degeneration: An Academy IRIS® Registry Analysis. Ophthalmic Surg Lasers Imaging Retina. 2024 Feb 01; 1-8.
- Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study. Invest Ophthalmol Vis Sci. 2024 Feb 01; 65(2):35.
- The epidemiology of pediatric dry eye disease in the United States: An IRIS® registry (Intelligent Research in Sight) analysis. Ocul Surf. 2024 Jan 28; 32:106-111.
- Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Sci Transl Med. 2024 Jan 24; 16(731):eadg4517.
- Factors associated with the use of botulinum toxin injections for adult strabismus in the IRIS Registry. J AAPOS. 2024 Jan 19; 103817.
- Surgical Approach and Reoperation Risk in Intermittent Exotropia in the IRIS Registry. JAMA Ophthalmol. 2024 Jan 01; 142(1):48-52.
- Response to the Comment on Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGpt in Uveitis. Ocul Immunol Inflamm. 2023 Dec 22; 1-2.
- Characterizing Macular Neovascularization in Myopic Macular Degeneration and Age-Related Macular Degeneration Using Swept Source OCTA. Clin Ophthalmol. 2023; 17:3855-3866.
- Comparison of Structural and Functional Features in Primary Angle-Closure and Open-Angle Glaucomas. J Glaucoma. 2023 Nov 28.
- Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma. Transl Vis Sci Technol. 2023 11 01; 12(11):12.
- Chatbots Vs. Human Experts: Evaluating Diagnostic Performance of Chatbots in Uveitis and the Perspectives on AI Adoption in Ophthalmology. Ocul Immunol Inflamm. 2023 Oct 13; 1-8.
- Normative Percentiles of Retinal Nerve Fiber Layer Thickness and Glaucomatous Visual Field Loss. Transl Vis Sci Technol. 2023 10 03; 12(10):13.
- Evaluating the Diagnostic Accuracy and Management Recommendations of ChatGPT in Uveitis. Ocul Immunol Inflamm. 2023 Sep 18; 1-6.
- The Epidemiology and Risk Factors for the Progression of Sympathetic Ophthalmia in the United States: An IRIS Registry Analysis. Am J Ophthalmol. 2024 Feb; 258:208-216.
- Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma. IEEE J Biomed Health Inform. 2023 09; 27(9):4329-4340.
- Risk Factors for Glaucoma Diagnosis and Surgical Intervention following Pediatric Cataract Surgery in the IRIS® Registry. Ophthalmol Glaucoma. 2023 Sep 06.
- Deep Ocular Phenotyping Across Primary Open-Angle Glaucoma Genetic Burden. JAMA Ophthalmol. 2023 09 01; 141(9):891-899.
- Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks. medRxiv. 2023 Aug 23.
- An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging. Ophthalmol Sci. 2024 Mar-Apr; 4(2):100389.
- Participant Experience Using Novel Perimetry Tests to Monitor Glaucoma Progression. J Glaucoma. 2023 11 01; 32(11):948-953.
- Polygenic Risk Score Improves Prediction of Primary Open Angle Glaucoma Onset in the Ocular Hypertension Treatment Study. medRxiv. 2023 Aug 16.
- Metabolite and Lipid Biomarkers Associated With Intraocular Pressure and Inner Retinal Morphology: 1H NMR Spectroscopy Results From the UK Biobank. Invest Ophthalmol Vis Sci. 2023 08 01; 64(11):11.
- Deep Learning for Localized Detection of Optic Disc Hemorrhages. Am J Ophthalmol. 2023 11; 255:161-169.
- Structure-function associations between contrast sensitivity and widefield swept-source optical coherence tomography angiography in diabetic macular edema. Graefes Arch Clin Exp Ophthalmol. 2023 Nov; 261(11):3113-3124.
- The prevalence and recurrence risk of bare sclera pterygium surgery in the United States. Ocul Surf. 2023 07; 29:547-549.
- Plasma metabolite profile for primary open-angle glaucoma in three US cohorts and the UK Biobank. Nat Commun. 2023 05 19; 14(1):2860.
- Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes. medRxiv. 2023 May 17.
- Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting. PLOS Digit Health. 2023 May; 2(5):e0000240.
- Factors Associated With Nasolacrimal Duct Probing Failure Among Children in the Intelligent Research in Sight Registry. JAMA Ophthalmol. 2023 04 01; 141(4):342-348.
- Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline. Br J Ophthalmol. 2023 Mar 29.
- Comparison of Perimetric Outcomes from a Tablet Perimeter, Smart Visual Function Analyzer, and Humphrey Field Analyzer. Ophthalmol Glaucoma. 2023 Sep-Oct; 6(5):509-520.
- PyVisualFields: A Python Package for Visual Field Analysis. Transl Vis Sci Technol. 2023 02 01; 12(2):6.
- Smoking Is Associated With a Higher Risk of Surgical Intervention for Thyroid Eye Disease in the IRIS Registry. Am J Ophthalmol. 2023 05; 249:174-182.
- Risk Factors Associated With Pterygium Reoperation in the IRIS Registry. JAMA Ophthalmol. 2022 11 01; 140(11):1138-1141.
- Effectiveness of Microinvasive Glaucoma Surgery in the United States: Intelligent Research in Sight Registry Analysis 2013-2019. Ophthalmology. 2023 03; 130(3):242-255.
- Structure-function association between contrast sensitivity and retinal thickness (total, regional, and individual retinal layer) in patients with idiopathic epiretinal membrane. Graefes Arch Clin Exp Ophthalmol. 2023 Mar; 261(3):631-639.
- Visual Field Prediction: Evaluating the Clinical Relevance of Deep Learning Models. Ophthalmol Sci. 2023 Mar; 3(1):100222.
- Archetypal analysis of visual fields in optic neuritis reveals functional biomarkers associated with outcome and treatment response. Mult Scler Relat Disord. 2022 Nov; 67:104074.
- Effectiveness of Trabeculectomy and Tube Shunt with versus without Concurrent Phacoemulsification: Intelligent Research in Sight Registry Longitudinal Analysis. Ophthalmol Glaucoma. 2023 Jan-Feb; 6(1):42-53.
- Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns. Transl Vis Sci Technol. 2022 07 08; 11(7):21.
- Interaction of background genetic risk, psychotropic medications, and primary angle closure glaucoma in the UK Biobank. PLoS One. 2022; 17(6):e0270530.
- Neurotrophic Keratopathy in the United States: An Intelligent Research in Sight Registry Analysis. Ophthalmology. 2022 11; 129(11):1255-1262.
- Impact of the Affordable Care Act on Glaucoma Severity at First Presentation. Ophthalmic Epidemiol. 2023 06; 30(3):326-329.
- An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence. J Glaucoma. 2022 08 01; 31(8):626-633.
- Race and Ethnicity Differences in Disease Severity and Visual Field Progression Among Glaucoma Patients. Am J Ophthalmol. 2022 10; 242:69-76.
- Improving Visual Field Forecasting by Correcting for the Effects of Poor Visual Field Reliability. Transl Vis Sci Technol. 2022 05 02; 11(5):27.
- Adjustable Suture Technique Is Associated with Fewer Strabismus Reoperations in the Intelligent Research in Sight Registry. Ophthalmology. 2022 09; 129(9):1028-1033.
- Remote Video Monitoring of Simultaneous Visual Field Testing. J Glaucoma. 2022 07 01; 31(7):488-493.
- Assessing Surface Shapes of the Optic Nerve Head and Peripapillary Retinal Nerve Fiber Layer in Glaucoma with Artificial Intelligence. Ophthalmol Sci. 2022 Sep; 2(3):100161.
- Risk Factors for Glaucoma Drainage Device Revision or Removal Using the IRIS Registry. Am J Ophthalmol. 2022 08; 240:302-320.
- Unsupervised Machine Learning Shows Change in Visual Field Loss in the Idiopathic Intracranial Hypertension Treatment Trial. Ophthalmology. 2022 08; 129(8):903-911.
- Background polygenic risk modulates the association between glaucoma and cardiopulmonary diseases and measures: an analysis from the UK Biobank. Br J Ophthalmol. 2023 08; 107(8):1112-1118.
- Nonperfusion Area and Other Vascular Metrics by Wider Field Swept-Source OCT Angiography as Biomarkers of Diabetic Retinopathy Severity. Ophthalmol Sci. 2022 Jun; 2(2).
- Photoreceptor Layer Thinning Is an Early Biomarker for Age-Related Macular Degeneration: Epidemiologic and Genetic Evidence from UK Biobank OCT Data. Ophthalmology. 2022 06; 129(6):694-707.
- Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis. Transl Vis Sci Technol. 2022 01 03; 11(1):27.
- Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature. Circulation. 2022 01 11; 145(2):134-150.
- Reading cognition from the eyes: association of retinal nerve fibre layer thickness with cognitive performance in a population-based study. Brain Commun. 2021; 3(4):fcab258.
- Association Between Diabetes, Diabetic Retinopathy, and Glaucoma. Curr Diab Rep. 2021 09 08; 21(10):38.
- Renal function and lipid metabolism are major predictors of circumpapillary retinal nerve fiber layer thickness-the LIFE-Adult Study. BMC Med. 2021 09 07; 19(1):202.
- Wide-field swept-source optical coherence tomography angiography in the assessment of retinal microvasculature and choroidal thickness in patients with myopia. Br J Ophthalmol. 2023 01; 107(1):102-108.
- Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence. Transl Vis Sci Technol. 2021 08 02; 10(9):16.
- Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension. Transl Vis Sci Technol. 2021 08 02; 10(9):37.
- Usage Patterns of Minimally Invasive Glaucoma Surgery (MIGS) Differ by Glaucoma Type: IRIS Registry Analysis 2013-2018. Ophthalmic Epidemiol. 2022 08; 29(4):443-451.
- Temporal Trends in the Treatment of Proliferative Diabetic Retinopathy: An AAO IRIS® Registry Analysis. Ophthalmol Sci. 2021 Sep; 1(3):100037.
- The Effect of Ametropia on Glaucomatous Visual Field Loss. J Clin Med. 2021 Jun 25; 10(13).
- Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression. Transl Vis Sci Technol. 2021 06 01; 10(7):27.
- Age, Gender, and Laterality of Retinal Vascular Occlusion: A Retrospective Study from the IRIS® Registry. Ophthalmol Retina. 2022 02; 6(2):161-171.
- Structure-Function Mapping Using a Three-Dimensional Neuroretinal Rim Parameter Derived From Spectral Domain Optical Coherence Tomography Volume Scans. Transl Vis Sci Technol. 2021 05 03; 10(6):28.
- Variability and Power to Detect Progression of Different Visual Field Patterns. Ophthalmol Glaucoma. 2021 Nov-Dec; 4(6):617-623.
- Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning. PLoS One. 2021; 16(4):e0249856.
- Trends and Usage Patterns of Minimally Invasive Glaucoma Surgery in the United States: IRIS® Registry Analysis 2013-2018. Ophthalmol Glaucoma. 2021 Nov-Dec; 4(6):558-568.
- Chemical and thermal ocular burns in the United States: An IRIS registry analysis. Ocul Surf. 2021 07; 21:345-347.
- Characteristics of p.Gln368Ter Myocilin Variant and Influence of Polygenic Risk on Glaucoma Penetrance in the UK Biobank. Ophthalmology. 2021 09; 128(9):1300-1311.
- Predicting Global Test-Retest Variability of Visual Fields in Glaucoma. Ophthalmol Glaucoma. 2021 Jul-Aug; 4(4):390-399.
- Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility. Transl Vis Sci Technol. 2020 11; 9(12):22.
- Three-dimensional Neuroretinal Rim Thickness and Visual Fields in Glaucoma: A Broken-stick Model. J Glaucoma. 2020 10; 29(10):952-963.
- An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma. Transl Vis Sci Technol. 2020 08; 9(9):41.
- Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med Genomics. 2020 08 26; 13(1):120.
- Norms of Interocular Circumpapillary Retinal Nerve Fiber Layer Thickness Differences at 768 Retinal Locations. Transl Vis Sci Technol. 2020 08; 9(9):23.
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard. Ophthalmology. 2020 09; 127(9):1170-1178.
- Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence. JAMA Ophthalmol. 2020 02 01; 138(2):190-198.
- Baseline Age and Mean Deviation Affect the Rate of Glaucomatous Vision Loss. J Glaucoma. 2020 01; 29(1):31-38.
- Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. Ophthalmology. 2020 06; 127(6):731-738.
- Reply. Ophthalmology. 2019 10; 126(10):e78-e79.
- Sex-Specific Differences in Circumpapillary Retinal Nerve Fiber Layer Thickness. Ophthalmology. 2020 03; 127(3):357-368.
- The impact of artificial intelligence in the diagnosis and management of glaucoma. Eye (Lond). 2020 01; 34(1):1-11.
- Machine Learning in the Detection of the Glaucomatous Disc and Visual Field. Semin Ophthalmol. 2019; 34(4):232-242.
- Agreement and Predictors of Discordance of 6 Visual Field Progression Algorithms. Ophthalmology. 2019 06; 126(6):822-828.
- An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis. Invest Ophthalmol Vis Sci. 2019 01 02; 60(1):365-375.
- Predicting Refractive Outcome of Small Incision Lenticule Extraction for Myopia Using Corneal Properties. Transl Vis Sci Technol. 2018 Sep; 7(5):11.
- Reply. Ophthalmology. 2018 09; 125(9):e66-e67.
- Systemic and Ocular Determinants of Peripapillary Retinal Nerve Fiber Layer Thickness Measurements in the European Eye Epidemiology (E3) Population. Ophthalmology. 2018 10; 125(10):1526-1536.
- Quantifying positional variation of retinal blood vessels in glaucoma. PLoS One. 2018; 13(3):e0193555.
- The Interrelationship between Refractive Error, Blood Vessel Anatomy, and Glaucomatous Visual Field Loss. Transl Vis Sci Technol. 2018 Jan; 7(1):4.
- Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 1. Impacts of refractive error and interartery angle. J Biomed Opt. 2017 Dec; 22(12):1-11.
- Ametropia, retinal anatomy, and OCT abnormality patterns in glaucoma. 2. Impacts of optic nerve head parameters. J Biomed Opt. 2017 Dec; 22(12):1-9.
- Age, ocular magnification, and circumpapillary retinal nerve fiber layer thickness. J Biomed Opt. 2017 12; 22(12):1-19.
- Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma. Ophthalmology. 2018 03; 125(3):352-360.
- Impact of Natural Blind Spot Location on Perimetry. Sci Rep. 2017 07 21; 7(1):6143.
- Associations between Optic Nerve Head-Related Anatomical Parameters and Refractive Error over the Full Range of Glaucoma Severity. Transl Vis Sci Technol. 2017 Jul; 6(4):9.
- New Precision Metrics for Contrast Sensitivity Testing. IEEE J Biomed Health Inform. 2018 05; 22(3):919-925.
- Evaluation of the precision of contrast sensitivity function assessment on a tablet device. Sci Rep. 2017 04 21; 7:46706.
- Impact of anatomical parameters on optical coherence tomography retinal nerve fiber layer thickness abnormality patterns. SPIE Ophthalmic Technologies. 2017; XXVII:100450P.
- Combining retinal nerve fiber layer thickness with individual retinal blood vessel locations allows modeling of central vision loss in glaucoma. SPIE Ophthalmic Technologies. 2017; XXVII:100451M.
- The relationship between 3D morphology of optic disc and spatial patterns of visual field loss in glaucoma. SPIE Ophthalmic Technologies. 2017; XXVII:100451W.
- Relationship Between Central Retinal Vessel Trunk Location and Visual Field Loss in Glaucoma. Am J Ophthalmol. 2017 Apr; 176:53-60.
- Patterns of Retinal Nerve Fiber Layer Loss in Different Subtypes of Open Angle Glaucoma Using Spectral Domain Optical Coherence Tomography. J Glaucoma. 2016 10; 25(10):865-872.
- Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic. Curr Eye Res. 2017 04; 42(4):568-574.
- Patterns of functional vision loss in glaucoma determined with archetypal analysis. J R Soc Interface. 2015 Feb 06; 12(103).
- An evaluation of organic light emitting diode monitors for medical applications: great timing, but luminance artifacts. Med Phys. 2013 Sep; 40(9):092701.
- Temporal properties of liquid crystal displays: implications for vision science experiments. PLoS One. 2012; 7(9):e44048.
- A Predictive Approach to Nonparametric Inference for Adaptive Sequential Sampling of Psychophysical Experiments. J Math Psychol. 2012 Jun 01; 56(3):179-195.
- A computational model of dysfunctional facial encoding in congenital prosopagnosia. Neural Netw. 2011 Aug; 24(6):652-64.
- Chinese characters reveal impacts of prior experience on very early stages of perception. BMC Neurosci. 2011 Jan 26; 12:14.
- Deficits in long-term recognition memory reveal dissociated subtypes in congenital prosopagnosia. PLoS One. 2011 Jan 25; 6(1):e15702.
- Misspecifications of stimulus presentation durations in experimental psychology: a systematic review of the psychophysics literature. PLoS One. 2010 Sep 29; 5(9).
- The early time course of compensatory face processing in congenital prosopagnosia. PLoS One. 2010 Jul 21; 5(7):e11482.
- Achieving precise display timing in visual neuroscience experiments. J Neurosci Methods. 2010 Aug 30; 191(2):171-9.
- Liquid crystal display response time estimation for medical applications. Med Phys. 2009 Nov; 36(11):4984-90.
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Detecting Vision Loss and its Progression
Many ophthalmic diseases have long onset periods during which their functional impairments are minor and unnoticed by the patient. Similarly, the diagnosis or functional worsening of an eye disease is challenging, as measurements are noisy, and the differentiation between true disease progression and random fluctuations or measurement artifacts is difficult.
For this project, Dr. Elze is applying bioinformatical methods to large sets of patient measurements. His goal is to develop novel methods to diagnose the onset and the progression of ophthalmic diseases like glaucoma, retinitis pigmentosa, or diabetic retinopathy. As an example from his previous achievements, he developed and evaluated a novel scheme of representative patterns for glaucomatous vision loss based on unsupervised machine learning.
Relationship Between Retinal Structure and Visual Function in Eye Diseases
While detailed functional measurements of ophthalmic vision loss are often noisy, time consuming, and exhausting for the patient, ocular imaging of retinal structure can often be performed conveniently for the patient within minutes or even seconds.
Dr. Elze is applying image processing and statistical learning methods to large sets of paired structure-function measurements of several eye diseases (diabetic retinopathy, glaucoma, macular degeneration) to predict the details of functional vision loss from two- or three-dimensional retinal imaging, like fundus photography or optical coherence tomography. As an example from previous achievements, Dr. Elze identified a novel retinal biomarker (central retinal vessel trunk location) for central vision loss in glaucoma.
Aging, Lifestyle, and the Retina
Aging and lifestyle affect the eye and may be risk factors for eye diseases or confounders for their diagnosis.
In this project, Dr. Elze is participating in a population-based study with about 10,000 participants to systematically investigate the relationship between retinal parameters obtained via fundus photography and optical coherence tomography and age, as well as various groups of lifestyle-related variables, such as cardiovascular parameters (e.g., blood pressure, cholesterol, history of strokes), anthropometric parameters (e.g., body mass index, waist-to-hip ratio), substances (e.g., alcohol and tobacco consumption), or neurological diseases (e.g., Parkinson’s disease, neuro-cognitive disorders, multiple sclerosis).
Early Detection of Eye Diseases and of their Progression over Time
Many ophthalmic diseases have a long onset period before patients become aware of them. For instance, an estimated 25% of individuals with diabetes are undiagnosed in the United States. The disease silently affects their retina, often over many years. In fact, the retinal damage may affect functional vision so subtly that it initially goes unnoticed in the patient's daily life, requiring dedicated vision tests to be detected. Another eye disease with a high prevalence—glaucoma—often starts with small scotomas (blind spots) in the periphery, which are easy to ignore. Therefore, diseases like diabetic retinopathy or glaucoma often go unnoticed until the moderate-to-advances stages of the disease. At the same time, many ophthalmic diseases are irreversible, which makes early detection extremely important to timely initiate or adjust ocular therapy before diseases progress to stages that cause severe loss of quality of life.
Dr. Elze and his team aim to better characterize the functional loss from ocular diseases with the goal of developing novel diagnostic methods and improving the prediction of how some diseases may progress in future. For instance, he adjusted and applied statistical learning algorithms to identify representative patterns of vision loss from glaucoma. These patterns help to distinguish true glaucomatous functional loss from the various and frequent measurement artifacts and from damages caused by other eye diseases. Apart from the improvement of diagnostic and prognostic methods, Dr. Elze studies the optimal scheduling of patients, i.e., the optimal time intervals for patient testing to maximize the information gain of each test.
The translational potential of Dr. Elze's work related to diagnostic improvements is not only reflected by his publications, but also by the fact that he is co-inventor of two patents—namely one patent on modeling of glaucomatous visual field loss and another patent on an algorithm to determine the optimal scheduling intervals for patient testing.
Retinal Structure and Visual Function
Many ophthalmic diseases manifest themselves on the retina before functional deficits develop. Furthermore, even in the presence of functional damage, functional testing procedures can be time consuming and exhausting for patients. Ocular imaging, on the other hand, is convenient—even for patients at advanced age—and often takes only seconds to few minutes. Therefore, Dr. Elze and his team are investigating the relationship between damages of retinal structure and their precise effects on functional vision in eye diseases. In particular, Dr. Elze studies patient measurements from fundus images and optical coherence tomography and their association with specific effects on functional vision. For instance, his team recently identified an important retinal biomarker specifically for central vision loss in glaucoma.
How Lifestyle Affects the Eye
Many lifestyle-related parameters, such as obesity, high blood pressure, unfavorable cholesterol values, the lack of physical activity, or the consumption of alcohol or tobacco, may impair retinal structure. While lifestyle-related retinal changes are often too small to be noticed by people, they may impose risk factors for severe eye diseases, including potentially blinding optic neuropathies. Dr. Elze and his team are systematically investigating how aging and lifestyle factors affect the retina. They are participating in a population-based study (with about 10 thousand participants) that includes ocular imaging and a large number of physiological and cognitive parameters.