Computational Investigation of Glaucoma Progression
Glaucoma typically is accompanied by functional vision loss, called visual field defects, which may progress over time. That’s the reason why visual fields of glaucoma patients are regularly measured; however, these measurements are influenced by a number of factors and it is often hard for clinicians to decide whether changes over time reflect true changes of functional vision or are the result of normal measurement variations or artifacts (ie, one-time results). The aim of this project is to investigate the spatial configuration of glaucomatous visual field defects by a combination of mathematical algorithms and clinical expertise in order to identify patterns of disease progression. The resulting quantitative models will be implemented as a software which calculates for a given patient measurement the probability of if, how, and how quickly vision loss is expected to progress.
In this project, we identify representative patterns of the progression of glaucomatous vision loss and of glaucomatous nerve fiber damage by computational statistical learning procedures.
The first part of the project focuses on the problem that many glaucoma patients develop initially, which are small scotomas ("blind spots" in the field of vision) that may enlarge over time up to the stage of total blindness. However, the onset of individual vision loss, and the respective course of progression differs considerably from patient to patient. This substantial inter-individual variety makes the patient-specific prediction of vision loss progression extremely challenging. In previous studies, our lab combined a bioinformatical machine learning procedure (archetypal analysis) with ophthalmic background knowledge and identified representative patterns ("archetypes") of glaucomatous vision loss. As a first step of this project, we identify how these patterns change over time and determine the archetypes of vision loss progression.
In the second part of this project, we apply machine learning algorithms to determine the patterns of glaucomatous damage to the retina, in particular the thinning of the retinal nerve fiber layer. Subsequently, we relate these nerve fiber damage patterns to the patterns of vision loss progression by multivariate statistical procedures and develop a structure-function model of glaucoma progression. Finally, we implement this structure-function model as a software that reads retinal imaging scans and visual field measurements of patients and assists clinicians with diagnosis and prognosis of glaucoma.
While many previous studies investigated structure-function relationships in glaucoma and the progression of the disease, to date, the existing models are limited by the complexity of the different subtypes of the disease, each of which must be studied using dedicated bioinformatical methodology. The members of our lab combine skills from engineering and computer science with specific expertise in ophthalmology. Therefore, our group has the skills to process extraordinarily large and high-dimensional data sets to attack the problem of glaucoma progression at a unique level of detail in the course of this project.
Glaucoma is one of the leading causes of blindness worldwide. Once accomplished, this project will provide a clinical assistance software to predict patient-specific disease progression. We believe that our structure-function based software will help practitioners with clinical decisions about initiation or changes in ocular therapy. Thereby, this project may help to prevent the onset of functional vision loss, or progression of glaucoma, which poses a major threat of vision loss as a potentially blinding eye disease.
About the Researcher
Tobias Elze, PhD, is an investigator at Schepens Eye Research Institute and an instructor in ophthalmology at Harvard Medical School. After receiving his PhD in computer science, with an emphasis on visual neuroscience, through the Max Planck Institute for Mathematics in the Sciences, Dr. Elze joined the Schepens Eye Research Institute in 2011 as a postdoctoral fellow. At Schepens, he extended his research focus from basic vision science and visual psychophysics to ophthalmology. Dr. Elze joined the faculty of Harvard Medical School in 2013 and of Schepens Eye Research Institute in 2014. He contributes his expertise in computational statistics and machine learning to large-scale data analyses of clinical patient measurements. In particular, Dr. Elze studies the relationship between retinal structure and visual function in eye diseases like glaucoma, and aims to develop novel diagnostic and prognostic methods.
From early on in my life, on the one hand, I have been attracted by complex problems and theories as they typically occur in mathematics and basic sciences, but on the other hand, I have been interested in contributing to society and practical aspects of life. After nearly seven years of working in the field of theoretical neuroscience at the Max Planck Institute for Mathematics in the Sciences, I felt the desire to apply all the mathematical, computational, and theoretical skills I had acquired to an area of high relevance to humans and society. Therefore, I was intrigued to find that a postdoctoral research fellowship position posted at Schepens Eye Research Institute fully met my fields of interest: Working on projects that translate basic research to clinical practice in order to treat eye diseases and to increase the quality of life. After successfully applying, I moved from Germany to Boston in 2011. My expectations were met indeed, and it was a great pleasure for me to realize that my computational background allowed me to make useful contributions to the fields of medicine and health care. After my promotion to faculty at Schepens and Harvard Medical School, I have been guiding a talented and enthusiastic team with backgrounds in engineering and ophthalmology. While we do not work with patients ourselves, we closely collaborate with clinicians from Massachusetts Eye and Ear and other hospitals, and work with large data sets of patient measurements, which allows us to develop techniques and methods with immediate relevance to health care. The BrightFocus grant is of extraordinary importance for an early career scientist as myself: It enables our laboratory to develop foundations which we expect to build upon for many years in the future. The structure-function model for glaucoma that will be developed in the course of this project is of importance far beyond its immediate applications in clinical diagnosis, as we expect it to provide new insights in the mechanisms behind the disease, which may finally result in novel therapies that prevent blindness.
First published on: August 24, 2017
Last modified on: September 18, 2019