Ophthalmology (Eye & Ear Hospital) - Theses

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 71
  • Item
  • Item
    Thumbnail Image
    Development of a bioengineered corneal surface replacement
    Francis, David Patrick. (University of Melbourne, 2006)
  • Item
    Thumbnail Image
    Development of a bioengineered corneal surface replacement
    Francis, David Patrick. (University of Melbourne, 2006)
  • Item
    Thumbnail Image
    Reticular Pseudodrusen and their Impact in Age-Related Macular Degeneration
    Kumar, Himeesh ( 2023-09)
    Background: Age-related macular degeneration (AMD) is a leading cause of vision loss, with no treatments available to slow or stop the development of late, sight-threatening complications in the early stages of the disease. Reticular pseudodrusen (RPD), which are distinctive subretinal drusenoid deposits, have become increasingly appreciated as a potentially critical phenotype driving vision loss in AMD. There is thus a need to better understand RPD, and to develop tools to facilitate such work. Aims: To explore the association between RPD and the health and function of eyes with non-late AMD by characterising their relationship with photoreceptor function, and to better understand the association of RPD with late AMD development. Finally, to develop an automated method to segment RPD on optical coherence tomography (OCT) imaging. Methods: The association between RPD extent and location (determined on combined OCT and near-infrared reflectance imaging) and photoreceptor function, determined using mesopic fundus-controlled perimetry and dark adaptation chromatic perimetry, was evaluated in eyes with large drusen and no evidence of late AMD. The association between both RPD volume and imaging characteristics (derived using texture analysis), and AMD disease progression, was investigated in a cohort of individuals with bilateral large drusen followed over 3 years. Finally, a deep learning (DL) model was developed to segment RPD on OCT B-scans and its performance for this task was compared to four retinal specialists. The performance of this model to detect eyes with RPD was also evaluated in four external cohorts against two retinal specialists. Results: The increasing global, but not local, extent of RPD was significantly associated with local cone and rod photoreceptor dysfunction in eyes with large drusen, even after adjusting for other key confounders. No significant association was observed between increasing two- or three-dimensional RPD extent and an increased rate of developing late AMD. Five textural features of RPD were extracted to characterise the pattern and distribution of RPD deposits within an eye, and none of these features were found to be significantly associated with late AMD development. Finally, the DL segmentation model for RPD showed a comparable level of agreement with four retinal specialists as the level of inter-reader agreement. The DL model also showed comparable performance to two retinal specialists for the detection of RPD in 559 eyes from 503 participants in the four external cohorts. Conclusions: These findings show that there may be generalized pathogenic changes in eyes with RPD that account for the observed localised cone- and rod-mediated visual dysfunction that has not been previously appreciated. Further work to understand these potential pathogenic changes could help uncover the disease mechanisms underlying RPD. Future work to gain a further understanding of RPD could be facilitated by the DL model for RPD segmentation developed, which was shown to be robust and comparable with human experts. Uncovering the disease mechanisms driving the formation of RPD and how they drive vision loss in AMD could help identify new therapeutic targets for this phenotype and ultimately aid in preventing irreversible vision loss in AMD.
  • Item
    Thumbnail Image
    Predicting progression in age-related macular degeneration
    Goh, Kai Lyn ( 2023-08)
    Background: Predicting which individuals will develop vision-threatening complications of age-related macular degeneration (AMD) is a challenging task, as traditional models based on colour fundus photography (CFP) correctly identify less than half of those who subsequently develop late AMD at 95% specificity. Thus, there is a great need to improve risk stratification for individuals with the early stages of AMD. Aims: To examine the prognostic significance of novel pathological characteristics related to drusen phenotypes and pigmentary abnormalities in the early stages of AMD. Methods: Retinal imaging data from 280 eyes from 140 individuals with bilateral large drusen enrolled in a longitudinal observational study was evaluated. Individuals underwent multimodal imaging (MMI) and microperimetry at baseline and then 6-monthly for up to 3-years. Disease progression was primarily evaluated based on the development of MMI-defined late AMD, and secondarily based on the rate of visual sensitivity decline on microperimetry prior to late AMD development. Four retinal specialists assessed the likelihood that each eye at baseline would progress with CFP, and then with MMI, to determine if MMI improves their ability to predict late AMD development. Baseline images were assessed for the: (i) presence of cuticular drusen, and extent of (ii) hyporeflective cores within drusen (HCD), (iii) hyperpigmentary abnormalities (HPAs), and (iv) hyperreflective foci (HRF) that do not spatially correspond to HPAs [HRF(OCT+/CFP-)]. The association with progression and impact on visual sensitivity of each feature was examined, including adjustments for well-established risk factors for progression (drusen volume from optical coherence tomography, presence of pigmentary abnormalities on CFP, and age). Results: The prediction of late AMD development by retinal specialists was improved when using MMI compared to CFP. However, a basic prediction model (age, presence of pigmentary abnormalities, and drusen volume) outperformed clinicians. In this cohort, neither the presence of cuticular drusen nor extent of HCD were significantly associated with an increased rate of progression to late AMD, reduced mean visual sensitivity at baseline, or an increased rate of visual sensitivity decline, after adjusting for well-established risk factors of progression. The quantification of HPA extent did not significantly improve the prediction of late AMD development compared to HPA presence, and the addition of HRF(OCT+/CFP-) extent to HPA extent also did not improve performance. Both HPA and HRF(OCT+/CFP-) extent were independently associated with reduced sector-based visual sensitivity, with the latter also associated with a significantly faster rate of visual sensitivity decline. Conclusions: Accounting for drusen phenotypes such as cuticular drusen and HCD, or the quantity of HPAs and HRF(OCT+/CFP-), did not significantly improve the prediction of late AMD development above what could be achieved by well-established risk factors. However, a basic prediction model using these parameters – drusen volume, presence of pigmentary abnormalities and age – outperformed retinal specialists, suggesting that such a model could improve counselling and monitoring of individuals in clinical practice. Such a model could also be used to better identify an enriched cohort to improve feasibility of future interventional trials, and thus help expedite the discovery of preventative treatments in the early stages of AMD.
  • Item
    Thumbnail Image
    Using genetic technologies to understand and treat retinal degeneration
    Nguyen, Tu Thanh ( 2023-04)
    Advances in genetic technologies, such as CRISPR/Cas9, have revolutionised the way we study and manipulate the genome. The first generation of CRISPR/Cas was used for genome editing; subsequent advances have led to modifications of CRISPR/Cas for additional uses, such as activation and repression of gene expression. This opens up the possibility to manipulate endogenous gene expression, demonstrating CRISPR as a useful tool to study gene functions and control cell identity. This thesis examines how advanced genetic technologies can be used to understand gene functions and develop therapies for retinal degenerative diseases, including retinitis pigmentosa (RP) and age-related macular degeneration (AMD). Chapter 1 provides a project overview and discusses the use of genetic technologies to study gene functions and develop treatments for retinal degenerative diseases. Part I of this thesis, which includes Chapter 2, 3, and 4, focuses on the development of a gene therapy for RP. In Chapter 2, I provided an overview of RP, its genetic contribution and recent therapeutic approaches using gene therapy, as well as an introduction to cellular reprogramming as an alternative method to treat RP. In Chapter 3, I described the development of a direct reprogramming technology to convert human Muller glia (MG) into induced rod photoreceptors (iRods) in vitro by activating the expression of selected transcription factors. Different combinations of these factors were tested on a human MG cell line, MIO-M1, and several combinations that promoted reprogramming of MG into iRods in vitro were identified. RT-qPCR and immunocytochemistry results demonstrated activation of the photoreceptor marker rhodopsin (RHO) in iRods. Also, multi-electrode array analysis showed that the iRods possessed functional electrophysiology. Finally, single-cell transcriptome analysis was performed to profile the iRods. The results highlighted that iRods expressed specific rod markers and found two different trajectories for iRod reprogramming. Subsequently, reprogramming of MG into iRods was tested in vivo by viral delivery of reprogramming factors into the P23H-3 rat model of autosomal dominant RP (Chapter 4). Treatment with reprogramming cocktails AAV-ANNr and AAV-Nr2P led to functional rescue, as well as localised changes to retinal morphology. Part II of this thesis, which includes two published journal articles (Chapter 5 and 6), explores how genetic technologies can be used to study functions of AMD-related genes. Chapter 5 serves as an introduction to Part II and discusses new technologies to understand functions of genes implicated in AMD. Chapter 6 explores the use of CRISPR technology to investigate a novel AMD-associated gene called POLDIP2. A POLDIP2 knockout human retinal pigment epithelial (RPE) cell line was generated using CRISPR/Cas, which displayed normal levels of cell proliferation, cell viability, phagocytosis and autophagy. RNA sequencing of the POLDIP2 knockout cell line highlighted changes in genes related to immune response, complement activation, vascular development and oxidative damage, which are biological processes relevant to AMD. This study also reveals a novel link between POLDIP2 and the mitochondrial superoxide dismutase SOD2, suggesting a potential role of POLDIP2 in oxidative stress regulation in AMD pathology. Taken together, these results contribute to our understanding of the genetic factors involved in retinal degenerative diseases and the advancement of novel therapeutic approaches to restore vision in diseased eyes.
  • Item
    Thumbnail Image
    Embracing the Future: An Examination of the Potential Role of Artificial Intelligence in Ophthalmology
    Rothschild, Philip Samuel ( 2023-01)
    Background: Substantial developments in artificial intelligence (AI) hold promise for screening and diagnosing ophthalmic diseases. However significant technological advances can be disruptive. Similarly, technology development and adoption do not necessarily occur hand-in-hand. The successful adoption of AI technology in eye health will necessitate education of healthcare professionals as well as appropriate organisational and sector support. Furthermore, it is necessary to make sure that eye health professionals are actively engaged in the implementation of AI applications in the clinical setting, to ensure that they are safe, effective, and used appropriately. Aim: This body of research aims to explore the implementation challenges of AI within ophthalmology and to inform the development of educational frameworks for ophthalmologists and trainees. The aims include: (1) Exploring knowledge and expectations of clinicians in ophthalmology and related medical specialties about AI. (2) Evaluating the impact of AI assistance on the grading performance of eye health professionals engaged in diabetic retinopathy screening. (3) Informing the development of an AI educational curriculum framework for ophthalmology trainees. Methods: A survey of ophthalmologists, dermatologists, radiologists/radiation oncologists and their trainees in Australia and New Zealand was carried out to understand perceptions of AI within these fields of medicine. Furthermore, the impact of a deep learning program as a tool used by optometrists, orthoptists and trainees for grading fundus photographs for diabetic retinopathy (DR) was assessed. Impacts on grading accuracy, confidence, and speed were evaluated. Finally the perspectives of ophthalmology trainees about AI education was explored using focus groups, to help guide the development of a framework educational AI curriculum for ophthalmology trainees. Results: Artificial intelligence was acknowledged by ophthalmologists, dermatologists and radiologists/radiation oncologists to represent a significant advance in healthcare technology that will have a broad-ranging positive influence. Reducing time spent on repetitive tasks and improving access to disease screening were seen as major potential benefits of the use of AI, with education identified as an important factor in the proper preparation of clinicians. In keeping with this, the study of AI assistance in identifying referable DR indicated that AI support was associated with increased accuracy, speed and confidence of optometrists, orthoptists, and their trainees, validating the potential utility of the technology. Furthermore, the focus group study demonstrated that ophthalmology trainees were keen for their College to include instruction on the clinical use of AI in their training curriculum and that trainees were interested in gaining a basic understanding of the technology and its potential implications. Conclusion: Artificial intelligence is perceived by ophthalmologists to likely have a significant and positive effect on their specialty. This perspective was reinforced by ophthalmology trainees who outlined how best to include an approach to AI in their educational curriculum. One AI adjunct was found to assist with DR screening and this may prove to have broader implications for the treatment of eye disease and easing workflow pressures. Preparing for the implementation of AI and understanding how it may affect clinicians plays an important role in underpinning the success of the technology within the field of ophthalmology.
  • Item
    Thumbnail Image
    Using Cellular Reprogramming and CRISPR Technologies for the Study of Neurodegeneration and the Development of Cell-Based Therapies
    Urrutia Cabrera, Daniel ( 2023-05)
    The premise of my research is that gene expression can be used to modify cell identity to develop models for the study and treatment of neurodegeneration. Neurons are essential for a broad set of body processes, thus damage to neural systems generally results in debilitating and irreversible diseases that place a huge burden on societies. Therefore, there is a pressing need for the development of effective approaches to generate neural tissue, which can be used to study neurodegenerative disorders, test potential therapies and to ultimately treat neurodegeneration. Regenerative technologies such as cellular reprogramming and induced pluripotent stem cells (iPSC) offer hope for the treatment of degenerative and incurable diseases. I used a combinatorial approach of cellular reprogramming technologies and genetic engineering tools to enhance the potential of regenerative approaches. In this regard, the simplicity, programmability and specificity of CRISPR technology provides an excellent tool to regulate gene expression, which we harnessed to improve classical methods of differentiation and disease modelling. This thesis includes studies using a diverse range of technologies with great potential for retinal biology: 1) The use of iPSCs to develop a fast protocol for neuronal differentiation, which can provide in vitro models to study neurodegeneration; 2) Using cellular reprogramming to convert Mueller glia into cone photoreceptors; 3) Gene therapy to treat photoreceptor degeneration in vivo using viral vectors; 4) Genetic engineering with CRISPR technology to regulate gene expression and study gene function in age-related macular degeneration pathophysiology and cell fate modulation through cellular reprogramming.
  • Item
    Thumbnail Image
    Multimodal Retinal Imaging in Alzheimer's Disease
    Ashraf, Gizem ( 2023-02)
    Background There are over 44 million people living with dementia worldwide, and most have Alzheimer's disease (AD). The current diagnostic methods for AD include brain imaging modalities such as magnetic resonance imaging and positron emission tomography which require significant resources, cost, and time. In contrast, retinal imaging modalities are much more widely available, accessible, and are lower in cost. The retina is an extension of the brain and they share a common embryological origin, thus highlighting the potential role for retinal imaging in the diagnosis of AD. Aims The aims of this thesis are: firstly, to perform a systematic review and meta-analysis to understand and evaluate the current evidence regarding retinal imaging in AD. Secondly, to perform a cross-sectional study of retinal imaging in people with biomarker-defined AD compared to healthy controls by exploring the association between these biomarkers and retinal parameters measured by optical coherence tomography (OCT), optical coherence tomography-angiography (OCT-A), and hyperspectral imaging. Thirdly, to explore the role of multimodal imaging in AD by combining findings from various retinal imaging methods to assess the potential of a composite biomarker of AD. Methods A systematic review of PubMed, EMBASE and Scopus was performed in accordance with PRISMA guidelines. Random-effects meta-analyses of standardised mean difference, correlation and diagnostic accuracy were conducted. The findings of this review were used to guide a cross-sectional study, to examine retinal parameters from OCT, OCT-A, and hyperspectral imaging of 35 people with AD and 38 healthy controls. Finally, a literature review on multimodal imaging was performed, and a novel multimodal model was generated using a machine learning approach. Results Meta-analysis of previous studies demonstrated that in people with AD there was a trend towards thinning in most retinal layers on OCT, increased foveal avascular zone area on OCT-A, and reduced arteriole and venule fractal dimension on fundus photography. The cross-sectional study demonstrated that, when compared to healthy controls, AD patients had no significant differences in retinal thickening in most retinal layers on OCT, significantly higher numbers of branching arterioles and venules on OCT infrared en face images, increased foveal avascular zone area and vessel density changes on OCT-A, and increased hyperspectral scores on hyperspectral imaging. The literature review of multimodal imaging outlined current terminology, approaches and challenges in the field. Finally, a novel multimodal model was generated and its clinical utility and limitations were discussed. Conclusion Prior research has identified associations between a number of retinal imaging parameters and AD, however limitations in study design including small sample sizes and non-biological definition of AD cases combined with heterogeneity in imaging methods and reporting make it difficult to determine the utility of these changes as AD biomarkers. Additional retinal imaging biomarkers were found to be associated with AD in our cross-sectional study of a biologically defined AD cohort and the role of combining retinal imaging parameters from multiple imaging modalities in a multimodal approach was explored.
  • Item
    Thumbnail Image
    Machine learning approaches to identify early keratoconus and classify keratoconus progression
    Cao, Ke ( 2021)
    Background: Keratoconus (KC) is a common corneal condition affecting children and young adults that has remained a major cause of corneal transplant surgery during the last decade. A newer management method named corneal collagen crosslinking (CXL) may now be used to slow the progression of KC in many individuals. Several studies have shown that the introduction of CXL resulted in a decrease in corneal transplantation rates in many countries. The CXL strategy is most beneficial to patients in early stage of KC to preserve best visual outcomes. The progression of KC must also be documented before executing a CXL procedure on KC patients. Identifying individuals with early KC and progressive KC is thus critical for planning early intervention care. Despite these requirements, existing approaches to early KC diagnosis and progression detection are inefficient. This includes a subjective assessment of early KC, as well as the absence of standardised criteria for KC progression that has resulted in the absence of a globally accepted KC management plan. The advancement of corneal topography and tomography imaging equipment has dramatically increased the quality and quantity of data acquired in KC clinics. There are currently no tools available in clinics that allow for the analysis of all corneal topography data to identify early KC and progression of KC, further signalling that their existing decision-making process may be inadequate. Purpose: This thesis sought to combine all 1692 parameters (containing demographic information and examination characteristics that are not clinical measurements.) available from an advanced corneal tomography system known as Pentacam into the decision-making process for improving early KC identification and progression classification. To cope with large amounts of data, this thesis used a variety of machine learning algorithms to accomplish the following aims: 1. To evaluate the performance of a number of various machine learning algorithms for discrimination of early keratoconus from eyes without KC (control eyes) using 11 commonly derived KC parameters; 2. To explore the impact of using all available Pentacam parameters with the highest-performing machine learning model for detection of early keratoconus; 3. To identify a key set of parameters supporting detection of early keratoconus as an optimal subset of all Pentacam parameters; and 4. To improve the classification of keratoconus progression by stratifying longitudinal clinical changes in KC using all Pentacam parameters with unsupervised machine learning algorithms. Research Design and Methods: This thesis is part of the Australian Study of Keratoconus (ASK), and was undertaken on a retrospective cohort of 3042 KC eyes and 700 eyes without KC (control eyes) collected at the Royal Victorian Eye and Ear Hospital. The diagnosis of each subject was retrieved from their electronic medical records. Early KC eyes were further labelled from the KC group by an expert optometrist. Early KC was defined as having a normal appearance on slit-lamp biomicroscopy and retinoscopy examination and abnormal corneal topography, such as inferior-superior localised steepening or an asymmetric bowtie pattern. The fellow eye may or may not be affected by KC. All 1692 accessible parameters on each individual eye were acquired on their first (baseline) visit and subsequent visits using the Pentacam v1.20r127 (Oculus, Wetzlar, Germany). Two clinical measurements, vision and refraction, were obtained from the patient's electronic medical record (where available) and included in the analysis for aim 3. From aims 1 to 3, supervised machine learning methods were used to develop models for distinguishing early KC and control eyes while in aim 4, an unsupervised machine learning method was used. Results: In Aim 1, the random forest algorithm was found to be the optimal machine learning algorithm to detect early KC from control eyes in the current dataset. This result was obtained by comparing the performance of eight different machine learning algorithms across 11 commonly derived KC parameters using 49 early KC eyes (49 patients) and 39 control eyes (39 patients). Notably, Aim 1 proved the usefulness of performing a machine learning algorithm comparison and feature selection to identify an optimal model that provided the maximal model performance. Aim 2 established the effectiveness of integrating all Pentacam parameters in identifying early KC. Using a reduced dimensionality space of all Pentacam parameters, the random forest model achieved a 98% accuracy (97% sensitivity and 98% specificity) in detecting early KC with 145 early KC (141 patients) and 122 controls eyes (85 patients). This machine learning model outperformed the majority of established machine learning modes in the literature. Further, in Aim 3, the same dataset as in Aim 2 was analysed, and a key combination of Pentacam parameters was identified for recognising early KC. This key set of parameters included the eccentricity value at a 30-degree angle of the front cornea, the eccentricity value in the 9 mm diameter zone of the cornea, and the inferior versus superior corneal asymmetry. The random forest model developed using these parameters correctly identified 94% of eyes and had a sensitivity of 97% and a specificity of 91% for distinguishing early KC from controls in the internal test dataset. Additionally, Aim 3 revealed the beneficial impact of combining vision and refraction measurements towards early KC detection. Finally, for the progression study of KC in Aim 4, three clusters/subgroups were identified with KC clinical change, defined as rapid-change, moderate-change and limited-change groups. The clusters were derived using hierarchical clustering based on half-year longitudinal clinical changes in all Pentacam parameters in 903 KC (588 patients) and 119 control eyes (92 patients). In Aim 4, 39 corneal curvature-related parameters were also found to be significantly different across the three subgroups. Discussion: By incorporating all Pentacam parameters in the machine learning model, the machine learning methods created in this work considerably improve the completeness of early KC detection. This enables assessment of the whole cornea during the early KC evaluation, possibly boosting the accuracy of current approaches for identifying early KC. These findings demonstrate the value of incorporating more pertinent information in order to improve early KC identification. Additionally, a key set of parameters for diagnosing early KC was identified, and this finding established the vital importance of corneal eccentricity in the early identification of KC, and the pertinent parameters could be used in clinics for early detection. These findings increase the generalizability of early KC diagnosis. There are numerous corneal topography systems available for usage in various clinics, but no machine learning model created in the literature so far can be applied across multiple imaging systems. The machine learning models developed in this thesis have a greater chance of being applicable to a variety of imaging systems due to the small number of parameters required and their widespread availability. For the first time, a three-classification scheme for KC clinical changes associated with progression was established using data-driven clustering. The finding may change our view of disease progression, since there were more categories than previously thought, in addition to progressed and non-progressed KC. This classification may alter the therapy protocol for individuals with KC since the care of patients with varying degrees of progression may differ. Finally, the research emphasises the critical need of monitoring the complete corneal curvature while assessing KC progression, rather than concentrating just on the central corneal curvature, as is presently done in clinics. Conclusion: The use of machine learning indicates its ability to better define early KC as well as begin to dissect out key aspects of KC progression. The machine learning-based potential decision support systems developed in this thesis addressed several gaps in the field and have the potential to improve clinical examination of KC patients and lead to better patient management.