Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans

Retinal boundary segmentation in stargardt disease optical coherence tomography images using automated deep learning

Purpose: To use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease. Methods: Eighty-seven manually segmented (ground truth) OCT volume scan sets (5171 B-scans) from 22 patients with Stargardt disease were used for training, validation and testing of a novel retinal boundary detection approach (FS-GS) that combines a fully semantic deep learning segmentation method, which generates a per-pixel class prediction map with a graph-search method to extract retinal boundary positions. The performance was evaluated using the mean absolute boundary error and the differences in two clinical metrics (retinal thickness and volume) compared with the ground truth. The performance of a separate deep learning method and two publicly available software algorithms were also evaluated against the ground truth. Results: FS-GS showed an excellent agreement with the ground truth, with a boundary mean absolute error of 0.23 and 1.12 pixels for the internal limiting membrane and the base of retinal pigment epithelium or Bruch's membrane, respectively. The mean difference in thickness and volume across the central 6 mm zone were 2.10 µm and 0.059 mm3. The performance of the proposed method was more accurate and consistent than the publicly available OCTExplorer and AURA tools. Conclusions: The FS-GS method delivers good performance in segmentation of OCT images of pathologic retina in Stargardt disease. Translational Relevance: Deep learning models can provide a robust method for retinal segmentation and support a high-throughput analysis pipeline for measuring retinal thickness and volume in Stargardt disease

Softwarová aplikace pro archivaci a analýzu retinálních záznamů

There are several diagnostic medical devices for the diagnosis of eye pathologies. therein Retcam may be a fully integrated wide-field digital imaging system for the hospital and clinic. This provides ophthalmic visualization and photo documentation of retinal images. For every examination number of patients are high and therefore the images taken during the examination are more. In order to manage this, I proposed to develop a software application for archiving and analysis of retinal records. This web application connected to a database to save and retrieve patient records and retinal records for further analysis of pathology conditions of the eye are created in order to support the proposal. I’m using SQLite for the database it's linked with a server where the applicant details are received from the patient Form. Retinal images are saved within the system or Centralized Network Access Storage. And therefore, the path of the image is stored in a database for easy retrieving. Retinal images from the retcam are stored in a folder created by an application. Patient form created using Symfony PHP.Existuje několik diagnostických medicínských zařízení s cílem diagnostiky patologií očního systému jako je systém Retcam, který představuje plně integrovaný širokoúhlý zobrazovací systém pro klinické využití. Tento systém poskytuje oftalmologické vizualizace a fotografickou dokumentaci retinálních obrazů. V rámci oftalmologického vyšetření je produkováno větší množství obrazových záznamů, proto je potřeba řešit databázové systémy, kde tyto záznamy budou ukládány a archivovány. Pro tento účel je v rámci této diplomové práce navržena softwarová web aplikace pro archivaci a analýzu retinálních záznamů, která je propojena s retinální databází pro ukládání, správu retinálních obrazů. Je využíváno SQLite pro tvorbu databáze. Retinální obrazy jsou ukládány v rámci sytému, nebo centralizované sítě přístupového sdílení. Retinální obrazy jsou ukládány ve složkách, které definuje aplikace. Pacientské formuláře jsou vytvářeny s využitím nástroje symfony php.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers

3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.Comment: 27 pages, 19 figure

Zebrafish and mouse models for studying deubiquitinating enzyme genes as candidates for retinal dystrophies

[eng] The retina consists of several structured layers of highly specialized neurons that capture and process light stimuli enabling vision. Such a fine architecture turns retinal differentiation into an extremely complex event that must be accurately regulated. The ubiquitin-proteasome system (UPS) is considered one of the most dynamic and versatile mechanisms of protein regulation in eukaryotic cells. As ubiquitination is reversible, deubiquitinating enzymes (DUBs) play a major regulatory role in the UPS. Despite the importance of proteostasis and the UPS in health and disease, a more comprehensive in-depth analysis of DUB expression and function on particular tissues or organs, such as the retina, is still missing. Combining expression quantification, mRNA localization assays and functional analyses in animal and cellular models, we analyzed the function of several DUB genes in the retina to identify DUBs that regulate important retinal cell mechanisms, explore their relevance in retinal function in health and disease, and finally, posit them as new potential candidate genes for retinal dystrophies. Taking into consideration our results in the expression levels and pattern of DUBs in the retina, we first selected USP45 to perform functional assays in animal models in order to define its role and function in the retina. By morpholino-knockdown of usp45 in zebrafish embryos, our results showed moderate to severe eye morphological defects, eye size reduction, small body size with small tail or without tail, and disruption in notochord formation. There is also defective lamination and formation of the retinal structures, with no distinguishable layers and smaller retinas. Overall, our results supported the relevance of USP45 in the normal development and formation of the vertebrate retina, and we proposed this gene as a good candidate for causing hereditary retinal dystrophies, as later confirmed by other authors in several families. We also selected ATXN3, a DUB gene that causes the dominant polyQ disease Spinocerebellar ataxia type 3 (SCA3), and we aimed to analyze its function in the retina. We showed that depletion of Atxn3 in zebrafish and mice caused retinal morphological and functional alterations with photoreceptor outer segment elongation, cone opsin mislocalization, and cone hyperexcitation upon light stimuli. A pool of ATXN3 resides at the basal body and axoneme of the photoreceptor cilium, where it controls the levels and recruitment of the regulatory proteins KEAP1 and HDAC6. Abrogation of Atxn3 expression causes delayed phagosome maturation in the retinal pigment epithelium. We propose that ATXN3 regulates two relevant biological processes in the retina, ciliogenesis and phagocytosis, by modulating microtubule polymerization and microtubule-dependent retrograde transport, and propose ATXN3 as a causative or modifier gene in retinal/macular dystrophies. We further aimed to explore whether the SCA3 humanized mouse model showed specific retinal phenotype traits. We showed that polyQ-expanded ATXN3 protein formed a high number of progressive pathogenic aggregates in the retinal layers of transgenic Atxn3Q84 mice, and caused a decrease in the number of cone photoreceptors. Optical coherence tomography revealed a general decrease in the thickness of the retinal layers whereas retinal electrophysiological analyses showed a strong decrease in photoreceptor response to light, thus supporting severe retinal dysfunction in Atxn3Q84 mice. Similar analyses in human patients detected a correlation of retinal alterations with the number of CAG repeats and the age of onset of SCA3 symptoms. We propose that retinal alterations detected by non-invasive eye examination and electroretinography tests in SCA3 patients could serve as a valuable early-onset symptom and a biological marker of disease progression. As a conclusion, our work posits several DUB genes as candidates for inherited retinal dystrophies, but further investigation is needed to dissect the function of DUBs in retinal cell differentiation, photoreceptor function, and retinal homeostasis

Optical Studies of Oxidative Stress in Lung Tissue: Rodent Models

Objectives: There currently exists a need for reliable measurements of tissue metabolic state at cellular levels. The objective of this research was to study tools capable of evaluating cellular redox states in intact tissue. To meet this goal, three different instruments (cryoimager, fluorometer, and fluorescent microscope) were used to study the metabolism and functions of the mitochondria at different levels and regimes (cryo, ex vivo, in vivo and in vitro). Introduction: Through optical monitoring of autofluorescent mitochondrial metabolic coenzymes, as well as exogenous fluorophores, the state of mitochondria can be probed in real time in many intact organs and in vitro. Autofluorescent mitochondrial metabolic coenzymes, studied here, include NADH (nicotinamide adenine dinucleotide) and FAD (flavin adenine dinucleotide), and the ratio of these fluorophores, referred to as the mitochondrial redox ratio (RR), can be used as a quantitative metabolic marker of the tissue. Exogenous fluorophores include but are not limited to tetramethylrhodamine (TMRM) and Mito-SOX, which are used to evaluate the mitochondrial membrane potential and level of reactive oxygen species (ROS) in the mitochondria, respectively. Methods: Different optical imaging and acquisition techniques were studied to evaluate oxidative stress in lung tissue and cells in cryogenic temperatures, in vivo, ex vivo, and in vitro. Though in essence the underlying technological and biological principles appear to be the same, imaging in each of these regimes imposed unique challenges requiring significantly different approaches to system design, data acquisition, and processing. A brief description of each technique is provided here and each is described in detail in the following chapters. The first device utilized is a cryoimager, which sequentially slices tissue and acquires fluorescence images of up to five fluorophores in cryogenic temperatures (-40oC). Rapid freezing of organs preserves the tissue\u27s metabolic state and subsequent low temperature fluorescence imaging (cryoimaging) provides high fluorescence quantum yield as compared with room temperature. Sequential slicing of the tissue provides 3D spatial distribution of NADH and FAD fluorescence intensities throughout the tissue. These studies were conducted using the cryoimager in the Biophotonics Lab on different models of lung injuries including ischemia, hyperoxia, and BPD (bronchopulmonary dysplasia). The second device is a fluorometer, which was designed and implemented in the Biophotonics Lab. It is capable of monitoring the dynamics of the metabolism of the tissue through the use of optical surface fluorescence measurements of NADH and FAD. The ratio of these fluorophores, referred to as the mitochondrial redox ratio (RR), can be used as a quantitative metabolic marker of tissue. Surface fluorescence signals from NADH and FAD were acquired in the absence (baseline) and presence of metabolic perturbers (e.g. pentachlorophenol, rotenone, or potassium cyanide), in the presence of blood, and eventually in vivo. The third instrument, a fluorescent microscope, is used to image slides and dishes containing stained cells (e.g. endothelial cells, perycites, or fibroblasts) from lungs, hearts, and retinas to study their structure and dynamics at cellular level. Images of retinas were classified as normal or injured using developed cytometry tools and morphologic parameters. For heart and lung, the dynamics of concentration of reactive oxygen species (mainly superoxide) and calcium is monitored over time in cultured live cells. Results: In the cryogenic temperatures, lung treatment with KCN (inhibitor of Complex IV), resulted in an increase in RR and sets the upper limit of the NADH signal level while injured lungs (BPD model, hyperoxia and IR) showed a more oxidized chain compared with control lungs, and as a result more oxidative stress. In ex vivo fluorometric studies, an increase in RR from chain inhibitors (including KCN and rotenone), and a decrease in the same due to an uncoupler (PCP), all from baseline was observed which was consistent with the cryoimaging results. The same experiments in isolated perfused lungs previously treated with hyperoxia showed the same direction but different levels indicating the impairment in different complexes due to hyperoxia. Segmentation algorithm developed here showed 90% accuracy comparing to manual counting, and studying the cells in retina slides confirms apoptosis and oxidative stress in retinas from injured mice. In live cells, studying the dynamics of calcium concentration in the presence of different perturbations enabled us to study the behavior of mitochondrial regulated calcium channels. Also, changes in the Mito-SOX channel gave us the dynamics of mitochondrial ROS in the presence of chain perturbers (chemicals and gas). Conclusion: Optical instrumentation combined with signal and image processing tools provide quantitative physiological and structural information of diseased tissue due to oxidative stress

Intensity Inhomogeneity Correction of SD-OCT Data Using Macular Flatspace

Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images

Anti-VEGF Treatment for Diabetic Macular Oedema: Clinical and Laboratory Insights

Diabetic retinopathy (DR) is a leading cause of vision impairment, characterised by vascular damage and neurodegeneration. Anti vascular endothelial growth factor (VEGF) drugs have revolutionised the management of the most common cause of vision impairment in DR, diabetic macular oedema (DMO). These drugs restore vision in DMO and induce regression of vascular changes in DR. Despite anti-VEGF therapy, a proportion of patients may have persistent DMO. The aim of the research presented in this thesis is to investigate the effect of switching therapy between two anti-VEGF drugs for persistent DMO and to assess the potential effects of anti-VEGF drugs in modulating neurodegeneration in DR through production of neurotrophic factors. In a prospective, single-arm, open-label clinical trial of patients with persistent DMO despite prior treatment with bevacizumab, there was a significant improvement in visual and anatomical outcomes when therapy was switched to aflibercept. Artifacts on automated optical coherence tomography calculations were increased in the presence of DMO. Peripheral ischaemia was associated with a poorer baseline vision and greater vision gain. Microperimetry outcomes correlated with objective and subjective vision outcomes. Diabetic conditions were simulated in vitro using ARPE-19 cell-line culture. There was downregulation of pigment epithelium derived factor (PEDF) expression in hypoxic states. In the absence of hypoxia, the addition of anti-VEGF drugs led to a significant downregulation of PEDF. Brain derived neurotrophic factor secretion was downregulated in high glucose states and upregulated in hypoxia. This thesis has addressed a number of key issues relating to persistent DMO, a management challenge with a poor evidence base. Further research is required into the identification of clinical and laboratory biomarkers to individualise pharmacotherapy and identify patients who may be poor and good responders to anti-VEGF therapy