520 research outputs found

    Galectins-1 and -3 Functions in Müller Glia during Retinal Development and Degeneration

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    Within the retina, Müller glial cells (MGCs) are central regulators of metabolic homeostasis during normal function and during pathogenic states. MGCs are major producers of Galectins (Gal) -1 and -3, carbohydrate-binding proteins which have been found to be key regulators of various cellular processes. This study aimed to investigate the potential roles of Gal-1 and -3 during inflammatory stimuli, as well as during models of retinal development and degeneration. Treatment of MIO-M1 cells with the pro-inflammatory cytokine IL-1 resulted in significant upregulation of Gal-1 (p.05). Inhibition of Gal-3 by siRNA increased the rate of MIO-M1 cell proliferation (p<.001). TGF-β2 treatment increased the rate of migration of MIO-M1 cells (p<.01), but this effect was nullified following Gal-3 siRNA inhibition. During retinal organoid development, Gal-1 mRNA and protein expression peaked between days 10 and 30, whilst Gal-3 mRNA and protein expression increased steadily to peak at the end of the experimental period of 90 days. Using an experimental model of intraocular pressure elevation, Gal-3 protein expression was found to be upregulated in anterior chamber tissues including the iridocorneal angle and iris. Additional studies in a rodent model of optic neuropathy showed that MGC derived extracellular vesicles were effective in partially restoring retinal function at 2 weeks after treatment (p.05), for which further investigations are merited. Although the current study has provided a better understanding of the roles of Galectins during retinal development and degeneration, additional studies are warranted to elucidate the wider roles of Gal-1 and -3 during retinal degenerative diseases and explore any potential therapeutic applications of these molecules

    Cerebral Metamorphopsia: Perceived spatial distortion from lesions of the adult human central visual pathway

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    Metamorphopsia is the perceived visual illusion of spatial distortion. Cerebral causes of metamorphopsia are much less common than retinal or ocular causes. Cerebral metamorphopsia can be caused by lesions along the central visual pathway or as a manifestation of epileptogenic discharges. Geometric visual distortions may result from structural lesions of the central visual pathway after reorganisation of the retinotopic representation in the cortex. Very few experimental investigations have been performed regarding cerebral metamorphopsia as it is often viewed as a clinical curiousity and analysis of the perceived distortion is difficult due to its subjective nature. Investigations have been undertaken to understand cortical plasticity as an explanation for visual filling-in. There has been much interest in cortical reorganisation after injuries to the peripheral and central visual pathway. Behavioural experiments aimed at quantifying the possible visual spatial distortion surrounding homonymous paracentral scotomas may be able to demonstrate cortical reorganisation after brain-damage and provide clues regarding the neural processes of visual perception. The aims of the thesis are: 1. To identify which cases of metamorphopsia, both published and unpublished, might be a consequence of cortical spatial reorganisation of retinotopic projections. 2. To investigate perceptual spatial distortion surrounding homonymous paracentral scotomas in adults with isolated unilateral injuries of the striate cortex. A review of the literature describing cases of cerebral metamorphopsia was performed. Metamorphopsia caused by retinal or ocular pathology, psychiatric conditions, drugs or medications were excluded. A retrospective case series of eight patients with metamorphopsia from a cerebral cause was performed in two clinical neurology practices specialising in vision disorders. Two cases who suffered from paracentral homonymous scotomas due to isolated unilateral primary visual cortex (V1) lesions were identified from a Neuro-ophthalmology practice. Neuropsychophysical experiments to investigate visual spatial perception surrounding their scotomas were developed and tested using MATLAB and Psychtoolbox. The use of the term 'metamorphopsia' was only in reference to cases in which contours or lines were experienced as distorted. In the published literature, few cases of cerebral metamorphopsia have been identified as being potentially due to cortical reorganisation. The main result is a statistically significant visual spatial distortion in the visual field surrounding a paracentral homonymous scotoma when compared to a normal control. There is also significant distortion of perception in the subjects' "unaffected" visual hemifield. After lesions of V1, visual perceptual spatial distortions may occur in the visual field surrounding homonymous paracentral scotomas. The spatial distortion may also occur in the normal hemifield possibly due to long-range cortical connections crossing to the other hemisphere through the corpus callosum. A collaborative approach across disciplines within vision science is required to further investigate the mechanisms responsible for perceptual visual illusions. Behavioural testing in brain-damaged cases remains important in developing theories of normal visual processing. New neuroimaging and neuroscience techniques could then test these theories, furthering our understanding of visual perception. An understanding of normal visual perception could allow future modification of neuronal processes to harness cortical reorganisation and potentially restore functional vision in humans with lesions of the central visual pathway

    Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

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    Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing

    OPTICAL COHERENCE TOMOGRAPHY OPHTHALMIC SURGICAL GUIDANCE

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    Optical coherence tomography (OCT) performs high-resolution cross-sectional and volumetric tissue imaging in situ through the combination of confocal gating, coherence gating, and polarization gating. Because it is noninvasive, OCT has been used in multiple clinical applications such as tissue pathology assessment and interventional procedure guidance. Moreover, OCT can perform functional measurements such as phase-sensitive measurement of blood flow and polarization-sensitive measurement of tissue birefringence. These features made OCT one of the most widely used imaging systems in ophthalmology. In this thesis, we present several novel OCT methods developed for microsurgery guidance and OCT image analysis. The thesis mainly consists of five parts, which are shown as follows. First, we present a BC-mode OCT image visualization method for microsurgery guidance, where multiple sparsely sampled B-scans are combined to generate a single cross-sectional image with an enhanced instrument and tissue layer visibility and reduced shadowing artifacts. The performance of the proposed method is demonstrated by guiding a 30-gauge needle into an ex-vivo human cornea. Second, we present a microscope-integrated OCT guided robotic subretinal injection method. A workflow is designed for accurate and stable robotic needle navigation. The performance of the proposed method is demonstrated on ex-vivo porcine eye subretinal injection. Third, we present optical flow OCT technique that quantifies accurate velocity fields. The accuracy of the proposed method is verified through phantom flow experiments by using a diluted milk powder solution as the scattering medium, in both cases of advective flow and turbulent flow. Fourth, we present a wrapped Gaussian mixture model to stabilize the phase of swept source OCT systems. A closed-form iteration solution is derived using the expectation-maximization algorithm. The performance of the proposed method is demonstrated through ex-vivo, in-vivo, and flow phantom experiments. The results show its robustness in different application scenarios. Fifth, we present a numerical landmark localization algorithm based on a convolutional neural network and a conditional random field. The robustness of the proposed method is demonstrated through ex-vivo porcine intestine landmark localization experiments

    The application of artificial intelligence in glaucoma diagnosis and prediction

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    Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups

    Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

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    Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis

    Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

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    Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-
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