The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

: In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice

Diabetic eye: associated diseases, drugs in clinic, and role of self-assembled carriers in topical treatment

Introduction: Diabetes is a pandemic disease that causes relevant ocular pathologies. Diabetic retinopathy, macular edema, cataracts, glaucoma, or keratopathy strongly impact the quality of life of the patients. In addition to glycemic control, intense research is devoted to finding more efficient ocular drugs and improved delivery systems that can overcome eye barriers. Areas covered: The aim of this review is to revisit first the role of diabetes in the development of chronic eye diseases. Then, commercially available drugs and new candidates in clinical trials are tackled together with the pros and cons of their administration routes. Subsequent sections deal with self-assembled drug carriers suitable for eye instillation combining patient-friendly administration with high ocular bioavailability. Performance of topically administered polymeric micelles, liposomes, and niosomes for the management of diabetic eye diseases is analyzed in the light of ex vivo and in vivo results and outcomes of clinical trials. Expert opinion: Self-assembled carriers are being shown useful for efficient delivery of not only a variety of small drugs but also macromolecules (e.g. antibodies) and genes. Successful design of drug carriers may offer alternatives to intraocular injections and improve the treatment of both anterior and posterior segments diabetic eye diseasesThis project is funded by Horizon 2020 Marie Sklodowska-Curie Actions [grant agreement – No 813440]S

Role of color doppler imaging in early diagnosis and prediction of progression in glaucoma

This longitudinal and prospective study analyzes the ability of orbital blood flow measured by color Doppler imaging (CDI) to predict glaucoma progression in patients with glaucoma risk factors. Patients with normal perimetry but having glaucoma risk factors and patients in the initial phase of glaucoma were prospectively included in the study and divided, after a five-year follow-up, into two groups: “Progression” and “No Progression” based on the changes in the Moorfields regression analysis (MRA) classification of Heidelberg retina tomograph (HRT). An orbital CDI was performed in all patients and the parameters obtained were correlated with changes in HRT. A logistic discrimination function (LDF) was calculated for ophthalmic artery (OA) and central retinal artery (CRA) parameters. Receiver operating characteristics curves (ROC) were used to assess the usefulness of LDFs to predict glaucomatous progression. A total of 71 eyes were included. End-diastolic velocity, time-averaged velocity, and resistive index in the OA and CRA were significantly different ( ) between the Progression and No Progression groups. The area under the ROC curves calculated for both LDFs was of 0.695 (OA) and 0.624 (CRA). More studies are needed to evaluate the ability of CDI to perform early diagnosis and to predict progression in glaucoma in eyes

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

Penatalaksanaan Glaukoma Neovaskular

Glaukoma neovaskular ialah suatu keadaan dimana terjadinya proliferasi jaringan fibrovaskular pada iris yang menyebar hingga mencapai trabekular meshwork sehingga menyebabkan terjadinya peningkatan tekanan intraocular yang tinggi. Laporan kasus ini memaparkan mengenai seorang pria berusia 43 tahun datang ke Poliklinik Mata Rumah Sakit Umum Daerah Dr. Zainoel Abidin Banda Aceh dengan keluhan penglihatan kabur sejak 2 minggu pada kedua mata. Pada pemeriksaan fisik didapatkan keadaan sakit sedang, composmentis, TD 130/80, nadi 80 x/menit, RR 20 x/menit  dan T 36,8oC. Pemeriksaan oftalmologis berupa tajam penglihatan pada mata kanan yaitu 1/300 ph (-), mata  kiri 5/50 ph 5/20 dan dijumpai konjungtiva palpebral inferior hiperemis, injeksi siliar, neovaskularisasi iris, sklera hiperemis, refleks cahaya langsung dan tidak langsung tidak ada serta pupil dilatasi. TIO mata kanan 43,4 mmHg dan mata kiri 12,2 mmHg. Pada pemeriksaan segmen anterior mata kanan ditemukan adanya konjuntiva hiperemis, injeksi siliar, kedalaman bilik mata depan van Herick derajat III, neovaskularisasi  pada iris; kedalaman bilik mata depan kiri van Herick derajat II . Pemeriksaan segmen posterior  didapatkan CDR OD : 0,8 dengan penggaungan diskus optikus dan CDR OS : 0,6. Pemeriksaan lainnya yaitu perimetri dengan hasil yaitu adanya tunnel vision pada mata kanan dan kiri. Penderita didiagnosa dengan glaukoma neovaskular ODS. Penatalaksanaan berupa preparat potassium klorida 600 mg, asetazolamid 250 mg, obat tetes mata timolol maleat 0,5%,  obat tetes mata artificial tears

Microcirculatory model predicts blood flow and autoregulation range in the human retina:in vivo investigation with laser speckle flowgraphy

In this study, we mathematically predict retinal vascular resistance (RVR) and retinal blood flow (RBF), we test predictions using laser speckle flowgraphy (LSFG), we estimate the range of vascular autoregulation, and we examine the relationship of RBF with the retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC). Fundus, optical coherence tomography (OCT), and OCT-angiography images, systolic/diastolic blood pressure (SBP/DBP), and intraocular pressure (IOP) measurements were obtained float 36 human subjects. We modeled two circulation markers (RVR and RBF) and estimated individualized lower/higher autoregula tion limits (LARL/HARL), using retinal vessel calibers, fractal dimen- sion, perfusion pressure, and population-based hematocrit values. Quantitative LSFG waveforms were extracted from vessels of the same eyes, before and during IOP elevation. LSFG metrics explained most variance in RVR (R-2 =0.77/P = 6.9.10(-9)) and RBF (R-2 =0.65/P = 1.0.10(-6)), suggesting that the markers strongly reflect blood flow physiology. Higher RBF was associated with thicker RNFL (P = 4.0.10(-4)) and GCC (P = 0.003), thus also verifying agreement with structural measurements. LARL was at SBP/DBP of 105/65 mmHg for the average subject without arterial hypertension and at 115/75 mmHg for the average hypertensive subject. Moreover, during IOP elevation, changes in RBF were more pronounced than changes in RVR. These observations physiologically imply that healthy subjects are already close to LARL, thus prone to hypoperfusion. In conclusion, we modeled two clinical markers and described a novel method to predict individualized autoregulation limits. These findings could improve understanding of retinal perfusion and pave the way for personalized intervention decisions, when treating patients with coexisting ophthalmic and cardiovascular pathologies. NEW & NOTEWORTHY We describe and test a new approach to quantify retinal blood flow, based on standard clinical examinations and imaging techniques, linked together with a physiological model. We use these findings to generate individualized estimates of the autoregulation range. We provide evidence that healthy subjects are closer to the lower autoregulation limit than thought before. This suggests that some retinas are less prepared to withstand hypoperfusion, even after small intraocular pressure rises or blood pressure drops

On the Indeterminates of Glaucoma:the Controversy of Arterial Blood Pressure and Retinal Perfusion

Glaucoma is a chronic eye disease characterized by thinning of the retina, death of ganglion cells, and progressive loss of vision, eventually leading to blindness. The prevalence of glaucoma is estimated at 1-3% of those over 40 years old. With a constantly aging population, this number is expected to increase significantly over the next 10 years. Even with treatment, about 15% of people with glaucoma currently develop residual vision or tunnel vision and eventually become blind or partially sighted. The mechanisms behind ganglion cell death are poorly understood. Elevated eye pressure is the main risk factor for glaucoma, but treatment in the form of medication, laser, or surgery can only slow the decline, not stop it. In addition, high intraocular pressure is neither necessary nor sufficient for the development of glaucoma, indicating the existence of other unknown risk factors. It has been established that the death of ganglion cells results in a decreased oxygen demand and a concomitant decrease in blood flow. However, there is also a hypothesis that reduced or unstable blood supply is not only a consequence, but also a cause of glaucoma. This is known as the ‘chicken-egg’ dilemma in glaucoma. It is supported by the observation that the risk of developing glaucoma is higher in people with very low blood pressure (sometimes even as a result of overtreatment of high blood pressure).This dissertation is an attempt to methodically examine whether blood pressure can be linked to changes in the retina that could suggest susceptibility to glaucoma. For this purpose, we analyze epidemiological data from the Groningen Longitudinal Glaucoma Study, we use advanced imaging techniques to model the microcirculation, and we describe its relationship with the neural structure and oxygen consumption of the retina. We provide evidence leaning towards the existence of a vascular component, likely pertinent to glaucoma

Biometric Indices and Their Relation with Age, Sex and Ethnicity

Objective: The aim of the present study was to evaluate the association of biometric indices with age, gender and ethnicity.Patients and Methods:  Three hundred and seventy patients entered the study from Basir Eye Clinic refractive assessment clinic. Sociodemographic data was gathered. Ocular parameters for both eyes and corneal curvature were measured by immersion technique and manual keratometry, respectively.Results: Axial length was significantly higher among male patients (P = 0.01) and vitreous chamber depth was significantly higher in females (P = 0.02). Axial length and vitreous chamber depth parameters were significantly higher among Arab patients (P = 0.01) compared to Persian patients and there was no other significant differences between these two groups. A significant increase in lens thickness and mean K (P < 0.001, coefficient = 0.15 and 0.023 respectively) and a significant reduction with axial length, anterior chamber depth and vitreous chamber depth (P < 0.001, coefficient = - 0.31, - 0.10 and - 0.37 respectively) were observed in correlation with the age of participants.Conclusion: There was correlation between axial length, depth of the anterior chamber, vitreous chamber depth, lens thickness and mean k with age of the participants. Male subjects and specific ethnicities such as Arab patients tend to have higher axial length values.Keywords: Axial length, anterior chamber depth, vitreous chamber depth, sex, age, Iran