Diabetic Cataract—Pathogenesis, Epidemiology and Treatment

Cataract in diabetic patients is a major cause of blindness in developed and developing countries. The pathogenesis of diabetic cataract development is still not fully understood. Recent basic research studies have emphasized the role of the polyol pathway in the initiation of the disease process. Population-based studies have greatly increased our knowledge concerning the association between diabetes and cataract formation and have defined risk factors for the development of cataract. Diabetic patients also have a higher risk of complications after phacoemulsification cataract surgery compared to nondiabetics. Aldose-reductase inhibitors and antioxidants have been proven beneficial in the prevention or treatment of this sightthreatening condition in in vitro and in vivo experimental studies. This paper provides an overview of the pathogenesis of diabetic cataract, clinical studies investigating the association between diabetes and cataract development, and current treatment of cataract in diabetics

Potential Imaging Biomarkers in the Development and Progression of Diabetic Retinopathy

Diabetic retinopathy (DR) is the most prevalent microvascular complication of diabetes and a leading cause of preventable blindness in the working-age population. However, due to a lack of suitable biomarkers, its prediction in asymptomatic patients is insufficient. Currently, DR is diagnosed at a stage when typical morphologic lesions become fundoscopically visible. Yet, chronically elevated blood glucose levels lead to characteristic alterations in retinal vessel caliber, blood flow, oxygen saturation, and the capillary network, which precede DR lesions. Furthermore, emerging evidence suggests that retinal neurodegenerative changes occur early in diabetes, initiating a disintegration of the retinal neurovascular unit prior to the appearance of microvasculopathy in DR. This chapter will discuss recent research achievements toward understanding the complexities of DR pathophysiology. It will focus on the nomination of potential imaging biomarkers for the prediction of DR development and progression using innovative structural, functional, and metabolic imaging techniques, including optical coherence tomography angiography (OCTA), retinal oximetry, ultra-wide field FA, and corneal confocal microscopy (CCM). Validation of these biomarkers would allow the identification of patients at high risk of developing DR and might initiate a swift move to early diagnosis and individualized care

Three-Dimensional Topographic Angiography in Chorioretinal Vascular Disease

PURPOSE. To evaluate a new angiographic technique that offers three-dimensional imaging of chorioretinal vascular diseases. METHODS. Fluorescein (FA) and indocyanine green angiography (ICGA) were performed using a confocal scanning laser ophthalmoscope. Tomographic series with 32 images per set were taken over a depth of 4 mm at an image frequency of 20 Hz. An axial analysis was performed for each x/y position to determine the fluorescence distribution along the z-axis. The location of the onset of fluorescence at a defined threshold intensity was identified and a depth profile was generated. The overall results of fluorescence topography were displayed in a gray scale-coded image and three-dimensional relief. RESULTS. Topographic angiography delineated the choriocapillary surface covering the posterior pole with exposed larger retinal vessels. Superficial masking of fluorescence by hemorrhage or absorbing fluid did not preclude detection of underlying diseases. Choroidal neovascularization (CNV) appeared as a vascular formation with distinct configuration and prominence. Chorioretinal infiltrates exhibited perfusion defects with dye pooling. Retinal pigment epithelium detachments (PEDs) demonstrated dynamic filling mechanisms. Intraretinal extravasation in retinal vascular disease was detected within a well-demarcated area with prominent retinal thickening. CONCLUSIONS. Confocal topographic angiography allows highresolution three-dimensional imaging of chorioretinal vascular and exudative diseases. Structural vascular changes (e.g., proliferation) are detected in respect to location and size. Dynamic processes (e.g., perfusion defects, extravasation, and barrier dysfunction) are clearly identified and may be quantified. Topographic angiography is a promising technique in the diagnosis, therapeutic evaluation, and pathophysiological evaluation of macular disease. (Invest Ophthalmol Vis Sci. 2001;42: 2386 -2394 C horioretinal vascular disease of the macular area (e.g., diabetic maculopathy [DMP] and age-related macular degeneration [ARMD]) are the main reasons for progressive and severe visual loss by occlusive, proliferative, and/or exudative mechanisms. 1,2 Fluorescein angiography (FA) is the classic diagnostic tool but is often compromised by masking phenomena as a consequence of the short wavelength used. Diffuse leakage of the small fluorescein molecule causes further difficulties in identifying the origin and quantifying the dynamics of leakage. Despite stereoscopic viewing systems, many lesions remain occult, and prominence and extent of exudation are evaluated only subjectively. 2,3 Indocyanine green angiography (ICGA) is effective in the near-infrared spectrum which allows improved transmission, and, mostly bound to albumin, it is thought to extravasate minimally. 5-7 Scanning laser ophthalmoscopy (SLO), with point-source illumination and optimized excitation, has further enhanced diagnostic efficacy. 9,10 The option to scan through different retinal layers is nevertheless limited to a depth resolution of approximately 300 m. It may be used, however, to obtain topographic profiles of strongly reflecting intraocular structures, such as the optic disc and the macular region. 11 Morphometric imaging of vascular structures of retina and choroid would significantly improve the diagnosis of macular disease. A novel angiographic technology, confocal topographic angiography, has been developed that allows threedimensional (3-D) documentation of vascular structures and characterization of dynamic phenomena such as perfusion and leakage. The technique of topographic image processing was applied in the FA and ICGA analyses of representative types of chorioretinal vascular disease, to document structural and dynamic changes and to evaluate the diagnostic potential of the new method. MATERIALS AND METHODS The basic topographic principle is to use a series of lateral confocal optical sections of the chorioretinal fluorescence distribution and, by introducing a smart algorithm, to extract the 3-D profile of the surface of vascular structures and related leakage. Data acquisition was achieved with a conventional confocal scanning laser angiograph. Data processing and topographic analysis were performed on a standard desktop computer, using newly developed software. The method of confocal laser scanning topography based on ICGA has been published. 12,13 Data Acquisition FA and ICGA were performed using a confocal SLO (Heidelberg Retina Angiograph; Heidelberg Engineering, Dossenheim, Germany). Infrared images were taken for optical alignment with the fovea in the center of a 30°field corresponding to a retinal area of 9 ϫ 9 mm. For FA, 5 ml of 10% fluorescein solution (Alcon Pharma GmbH, Freiburg, Germany), an argon laser emitting at 488 nm for excitation, and filters blocking transmission of wavelengths below 510 nm were used for detection. For ICGA a 50-mg solution of ICG (ICG Pulsion, München, Germany) was administered intravenously, and excitation and detection were performed, using a diode laser emitting at 795 nm and blocking filters for wavelengths below 835 nm. The diameter of the excitation beam was 10 m at the retina. The Rayleigh range of the focal beam's waist determining depth resolution was 300 m. During the early transit phase, the scanning laser was focused onto the retinal vessels and the excitation intensity was adjusted to obtain adequate illumination. An additive ϩ3-diopter (D) refractive correction was added by using the internal focus adjustment to create a preretinal initial focus for complete sectioning of elevated lesions. An early FA/ICGA series of 32 tomographic sections was taken over a depth of 4 mm, each separated From th

Diabetic cataract - pathogenesis, epidemiology and treatment

Cataract in diabetic patients is a major cause of blindness in developed and developing countries. The pathogenesis of diabetic cataract development is still not fully understood. Recent basic research studies have emphasized the role of the polyol pathway in the initiation of the disease process. Population-based studies have greatly increased our knowledge concerning the association between diabetes and cataract formation and have defined risk factors for the development of cataract. Diabetic patients also have a higher risk of complications after phacoemulsification cataract surgery compared to nondiabetics. Aldose-reductase inhibitors and antioxidants have been proven beneficial in the prevention or treatment of this sightthreatening condition in in vitro and in vivo experimental studies. This paper provides an overview of the pathogenesis of diabetic cataract, clinical studies investigating the association between diabetes and cataract development, and current treatment of cataract in diabetics

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin) --------------- Accepted for publication in the "IEEE International Symposium on Biomedical Imaging (ISBI) 2019

Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data

Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare professionals. Such a capability, however, is of great importance since it can improve patient management by providing information on the speed of disease progression already at the admission stage, or it can enrich the clinical trials with fast progressors and avoid the need for control arms by the means of digital twins. In this work, we develop a deep learning method that models the evolution of age-related disease by processing a single medical scan and providing a segmentation of the target anatomy at a requested future point in time. Our method represents a time-invariant physical process and solves a large-scale problem of modeling temporal pixel-level changes utilizing NeuralODEs. In addition, we demonstrate the approaches to incorporate the prior domain-specific constraints into our method and define temporal Dice loss for learning temporal objectives. To evaluate the applicability of our approach across different age-related diseases and imaging modalities, we developed and tested the proposed method on the datasets with 967 retinal OCT volumes of 100 patients with Geographic Atrophy, and 2823 brain MRI volumes of 633 patients with Alzheimer's Disease. For Geographic Atrophy, the proposed method outperformed the related baseline models in the atrophy growth prediction. For Alzheimer's Disease, the proposed method demonstrated remarkable performance in predicting the brain ventricle changes induced by the disease, achieving the state-of-the-art result on TADPOLE challenge

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relative distances. Investigations of their asymptotic correlation as well as numerical experiments show that a projection does usually not satisfy both objectives at once. In a standard classification problem we determine projections on the input data that balance the objectives and compare subsequent results. Next, we extend our application of orthogonal projections to deep learning tasks and introduce a general framework of augmented target loss functions. These loss functions integrate additional information via transformations and projections of the target data. In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy