A Population-Based Ultra-Widefield Digital Image Grading Study for Age-Related Macular Degeneration-Like Lesions at the Peripheral Retina.

Our understanding of the relevance of peripheral retinal abnormalities to disease in general and in age-related macular degeneration (AMD) in particular is limited by the lack of detailed peripheral imaging studies. The purpose of this study was to develop image grading protocols suited to ultra-widefield imaging (UWFI) in an aged population

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

EyeArt + EyePACS: Automated Retinal Image Analysis For Diabetic Retinopathy Screening in a Telemedicine System

Telemedicine frameworks are key to screening the large, ever-growing diabetic population for preventable blindness due to diabetic retinopathy (DR). Integrating fully-automated screening systems in telemedicine frameworks will make DR screening more efficient, cost-effective, reproducible, and accessible. In this paper, we present the integration of EyeArt, an automated DR screening system, into EyePACS, a telemedicine system for DR screening used in diverse screening settings. EyeArt in- corporates novel image processing and analysis algorithms for assessing image gradability; enhancing images based on median filtering; detecting interest regions and localizing lesions based on multi-scale morphological analysis; and DR screening and thus achieves robustness to the large image variability seen in a telemedicine system such as EyePACS. EyeArt is implemented as a scalable, high-throughput cloud-based system to enable large-scale DR screening. We evaluate the safety and performance of EyeArt on a dataset with 434,023 images from 54,324 patient cases obtained from EyePACS. On this dataset, EyeArt’s screening sensitivity is 90% at specificity 60.8% and the area under the receiver operating characteristic curve (AUROC) is 0.883. In a setup where trained human graders review patient cases recommended for referral by EyeArt with low confidence, a workload reduction of 62% is possible. Therefore, EyeArt can be safely integrated into large real world telemedicine DR screening programs such as EyePACS helping reduce workload and increase efficiency and thus help in reducing vision loss due to DR through early detection and treatment

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Differential intensity contrast swept source optical coherence tomography for human retinal vasculature visualization

We demonstrate an intensity-based motion sensitive method, called differential logarithmic intensity variance (DLOGIV), for 3D microvasculature imaging and foveal avascular zone (FAZ) visualization in the in vivo human retina using swept source optical coherence tomog. (SS-OCT) at 1060 nm. A motion sensitive SS-OCT system was developed operating at 50,000 A-lines/s with 5.9 μm axial resoln., and used to collect 3D images over 4 mm^2 in a normal subject eye. Multiple B-scans were acquired at each individual slice through the retina and the variance of differences of logarithmic intensities as well as the differential phase variances (DPV) was calcd. to identify regions of motion (microvasculature). En face DLOGIV image were capable of capturing the microvasculature through depth with an equal performance compared to the DPV

Deep learning analysis of eye fundus images to support medical diagnosis

Machine learning techniques have been successfully applied to support medical decision making of cancer, heart diseases and degenerative diseases of the brain. In particular, deep learning methods have been used for early detection of abnormalities in the eye that could improve the diagnosis of different ocular diseases, especially in developing countries, where there are major limitations to access to specialized medical treatment. However, the early detection of clinical signs such as blood vessel, optic disc alterations, exudates, hemorrhages, drusen, and microaneurysms presents three main challenges: the ocular images can be affected by noise artifact, the features of the clinical signs depend specifically on the acquisition source, and the combination of local signs and grading disease label is not an easy task. This research approaches the problem of combining local signs and global labels of different acquisition sources of medical information as a valuable tool to support medical decision making in ocular diseases. Different models for different eye diseases were developed. Four models were developed using eye fundus images: for DME, it was designed a two-stages model that uses a shallow model to predict an exudate binary mask. Then, the binary mask is stacked with the raw fundus image into a 4-channel array as an input of a deep convolutional neural network for diabetic macular edema diagnosis; for glaucoma, it was developed three deep learning models. First, it was defined a deep learning model based on three-stages that contains an initial stage for automatically segment two binary masks containing optic disc and physiological cup segmentation, followed by an automatic morphometric features extraction stage from previous segmentations, and a final classification stage that supports the glaucoma diagnosis with intermediate medical information. Two late-data-fusion methods that fused morphometric features from cartesian and polar segmentation of the optic disc and physiological cup with features extracted from raw eye fundus images. On the other hand, two models were defined using optical coherence tomography. First, a customized convolutional neural network termed as OCT-NET to extract features from OCT volumes to classify DME, DR-DME and AMD conditions. In addition, this model generates images with highlighted local information about the clinical signs, and it estimates the number of slides inside a volume with local abnormalities. Finally, a 3D-Deep learning model that uses OCT volumes as an input to estimate the retinal thickness map useful to grade AMD. The methods were systematically evaluated using ten free public datasets. The methods were compared and validated against other state-of-the-art algorithms and the results were also qualitatively evaluated by ophthalmology experts from Fundación Oftalmológica Nacional. In addition, the proposed methods were tested as a diagnosis support tool of diabetic macular edema, glaucoma, diabetic retinopathy and age-related macular degeneration using two different ocular imaging representations. Thus, we consider that this research could be potentially a big step in building telemedicine tools that could support medical personnel for detecting ocular diseases using eye fundus images and optical coherence tomography.Las técnicas de aprendizaje automático se han aplicado con éxito para apoyar la toma de decisiones médicas sobre el cáncer, las enfermedades cardíacas y las enfermedades degenerativas del cerebro. En particular, se han utilizado métodos de aprendizaje profundo para la detección temprana de anormalidades en el ojo que podrían mejorar el diagnóstico de diferentes enfermedades oculares, especialmente en países en desarrollo, donde existen grandes limitaciones para acceder a tratamiento médico especializado. Sin embargo, la detección temprana de signos clínicos como vasos sanguíneos, alteraciones del disco óptico, exudados, hemorragias, drusas y microaneurismas presenta tres desafíos principales: las imágenes oculares pueden verse afectadas por artefactos de ruido, las características de los signos clínicos dependen específicamente de fuente de adquisición, y la combinación de signos locales y clasificación de la enfermedad no es una tarea fácil. Esta investigación aborda el problema de combinar signos locales y etiquetas globales de diferentes fuentes de adquisición de información médica como una herramienta valiosa para apoyar la toma de decisiones médicas en enfermedades oculares. Se desarrollaron diferentes modelos para diferentes enfermedades oculares. Se desarrollaron cuatro modelos utilizando imágenes de fondo de ojo: para DME, se diseñó un modelo de dos etapas que utiliza un modelo superficial para predecir una máscara binaria de exudados. Luego, la máscara binaria se apila con la imagen de fondo de ojo original en una matriz de 4 canales como entrada de una red neuronal convolucional profunda para el diagnóstico de edema macular diabético; para el glaucoma, se desarrollaron tres modelos de aprendizaje profundo. Primero, se definió un modelo de aprendizaje profundo basado en tres etapas que contiene una etapa inicial para segmentar automáticamente dos máscaras binarias que contienen disco óptico y segmentación fisiológica de la copa, seguido de una etapa de extracción de características morfométricas automáticas de segmentaciones anteriores y una etapa de clasificación final que respalda el diagnóstico de glaucoma con información médica intermedia. Dos métodos de fusión de datos tardíos que fusionaron características morfométricas de la segmentación cartesiana y polar del disco óptico y la copa fisiológica con características extraídas de imágenes de fondo de ojo crudo. Por otro lado, se definieron dos modelos mediante tomografía de coherencia óptica. Primero, una red neuronal convolucional personalizada denominada OCT-NET para extraer características de los volúmenes OCT para clasificar las condiciones DME, DR-DME y AMD. Además, este modelo genera imágenes con información local resaltada sobre los signos clínicos, y estima el número de diapositivas dentro de un volumen con anomalías locales. Finalmente, un modelo de aprendizaje 3D-Deep que utiliza volúmenes OCT como entrada para estimar el mapa de espesor retiniano útil para calificar AMD. Los métodos se evaluaron sistemáticamente utilizando diez conjuntos de datos públicos gratuitos. Los métodos se compararon y validaron con otros algoritmos de vanguardia y los resultados también fueron evaluados cualitativamente por expertos en oftalmología de la Fundación Oftalmológica Nacional. Además, los métodos propuestos se probaron como una herramienta de diagnóstico de edema macular diabético, glaucoma, retinopatía diabética y degeneración macular relacionada con la edad utilizando dos representaciones de imágenes oculares diferentes. Por lo tanto, consideramos que esta investigación podría ser potencialmente un gran paso en la construcción de herramientas de telemedicina que podrían ayudar al personal médico a detectar enfermedades oculares utilizando imágenes de fondo de ojo y tomografía de coherencia óptica.Doctorad

Effect of Uveal Melanocytes on Choroidal Morphology in Rhesus Macaques and Humans on Enhanced-Depth Imaging Optical Coherence Tomography.

PurposeTo compare cross-sectional choroidal morphology in rhesus macaque and human eyes using enhanced-depth imaging optical coherence tomography (EDI-OCT) and histologic analysis.MethodsEnhanced-depth imaging-OCT images from 25 rhesus macaque and 30 human eyes were evaluated for choriocapillaris and choroidal-scleral junction (CSJ) visibility in the central macula based on OCT reflectivity profiles, and compared with age-matched histologic sections. Semiautomated segmentation of the choriocapillaris and CSJ was used to measure choriocapillary and choroidal thickness, respectively. Multivariate regression was performed to determine the association of age, refractive error, and race with choriocapillaris and CSJ visibility.ResultsRhesus macaques exhibit a distinct hyporeflective choriocapillaris layer on EDI-OCT, while the CSJ cannot be visualized. In contrast, humans show variable reflectivities of the choriocapillaris, with a distinct CSJ seen in many subjects. Histologic sections demonstrate large, darkly pigmented melanocytes that are densely distributed in the macaque choroid, while melanocytes in humans are smaller, less pigmented, and variably distributed. Optical coherence tomography reflectivity patterns of the choroid appear to correspond to the density, size, and pigmentation of choroidal melanocytes. Mean choriocapillary thickness was similar between the two species (19.3 ± 3.4 vs. 19.8 ± 3.4 μm, P = 0.615), but choroidal thickness may be lower in macaques than in humans (191.2 ± 43.0 vs. 266.8 ± 78.0 μm, P < 0.001). Racial differences in uveal pigmentation also appear to affect the visibility of the choriocapillaris and CSJ on EDI-OCT.ConclusionsPigmented uveal melanocytes affect choroidal morphology on EDI-OCT in rhesus macaque and human eyes. Racial differences in pigmentation may affect choriocapillaris and CSJ visibility, and may influence the accuracy of choroidal thickness measurements

A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the current literature lacks an extensive survey on the diagnosis of Alzheimer's disease or cognitive impairment using OCT or OCTA. This has motivated us to do a comprehensive survey aimed at machine/deep learning scientists or practitioners who require an introduction to the problem. The paper contains 1) an introduction to the medical background of Alzheimer's Disease and Cognitive Impairment and their diagnosis using OCT and OCTA imaging modalities, 2) a review of various technical proposals for the problem and the sub-problems from an automated analysis perspective, 3) a systematic review of the recent deep learning studies and available OCT/OCTA datasets directly aimed at the diagnosis of Alzheimer's Disease and Cognitive Impairment. For the latter, we used Publish or Perish Software to search for the relevant studies from various sources such as Scopus, PubMed, and Web of Science. We followed the PRISMA approach to screen an initial pool of 3073 references and determined ten relevant studies (N=10, out of 3073) that directly targeted AD diagnosis. We identified the lack of open OCT/OCTA datasets (about Alzheimer's disease) as the main issue that is impeding the progress in the field.Comment: Submitted to Computerized Medical Imaging and Graphics. Concept, methodology, invest, data curation, and writing org.draft by Yasemin Turkan. Concept, method, writing review editing, and supervision by F. Boray Te