Quantitative characterization of retinal features in translated OCTA.

This study explores the feasibility of quantitative Optical Coherence Tomography Angiography (OCTA) features translated from OCT using generative machine learning (ML) for characterizing vascular changes in retina. A generative adversarial network framework was employed alongside a 2D vascular segmentation and a 2D OCTA image translation model, trained on the OCT-500 public dataset and validated with data from the University of Illinois at Chicago (UIC) retina clinic. Datasets are categorized by scanning range (Field of view) and disease status.

Artificial intelligence for retinal diseases.

Purpose: To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases.

Methods: We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR).

EYE-Llama, an in-domain large language model for ophthalmology.

Background: Training Large Language Models (LLMs) with in-domain data can significantly enhance their performance, leading to more accurate and reliable question-answering (QA) systems essential for supporting clinical decision-making and educating patients.

Methods: This study introduces LLMs trained on in-domain, well-curated ophthalmic datasets. We also present an open-source substantial ophthalmic language dataset for model training.

Quantitative Characterization of Retinal Features in Translated OCTA.

Purpose: This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware.

Methods: The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images.

Federated learning for diagnosis of age-related macular degeneration.

This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions.

Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models.

Diabetic retinopathy (DR) is a major cause of vision impairment in diabetic patients worldwide. Due to its prevalence, early clinical diagnosis is essential to improve treatment management of DR patients. Despite recent demonstration of successful machine learning (ML) models for automated DR detection, there is a significant clinical need for robust models that can be trained with smaller cohorts of dataset and still perform with high diagnostic accuracy in independent clinical datasets (i.

Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy.

With the progression of diabetic retinopathy (DR) from the non-proliferative (NPDR) to proliferative (PDR) stage, the possibility of vision impairment increases significantly. Therefore, it is clinically important to detect the progression to PDR stage for proper intervention. We propose a segmentation-assisted DR classification methodology, that builds on (and improves) current methods by using a fully convolutional network (FCN) to segment retinal neovascularizations (NV) in retinal images prior to image classification.

Automated Region of Interest Selection Improves Deep Learning-Based Segmentation of Hyper-Reflective Foci in Optical Coherence Tomography Images.

Hyperreflective foci (HRF) have been associated with retinal disease progression and demonstrated as a negative prognostic biomarker for visual function. Automated segmentation of HRF in retinal optical coherence tomography (OCT) scans can be beneficial to identify the formation and movement of the HRF biomarker as a retinal disease progresses and can serve as the first step in understanding the nature and severity of the disease. In this paper, we propose a fully automated deep neural network based HRF segmentation model in OCT images.

Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning-based Risk Stratification System Using US Cine-Clip Images.

Purpose: To develop a deep learning-based risk stratification system for thyroid nodules using US cine images.

Materials And Methods: In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS)-structured radiology reports were evaluated. A deep learning-based system that exploits the cine images obtained during three-dimensional volumetric thyroid scans and outputs malignancy risk was developed and compared, using fivefold cross-validation, against a two-dimensional (2D) deep learning-based model (Static-2DCNN), a radiomics-based model using cine images (Cine-Radiomics), and the ACR TI-RADS level, with histopathologic diagnosis as ground truth.

Imaging and artificial intelligence for progression of age-related macular degeneration.

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy.

Differential artery-vein analysis in quantitative retinal imaging: a review.

Quantitative retinal imaging is essential for eye disease detection, staging classification, and treatment assessment. It is known that different eye diseases or severity stages can affect the artery and vein systems in different ways. Therefore, differential artery-vein (AV) analysis can improve the performance of quantitative retinal imaging.

AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography.

This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features.

Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy.

Purpose: To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy.

Methods: A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification.

VASCULAR COMPLEXITY ANALYSIS IN OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF DIABETIC RETINOPATHY.

Purpose: This study aimed to verify the feasibility of using vascular complexity features for objective differentiation of controls and nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) patients.

Methods: This was a cross-sectional study conducted in a tertiary, subspecialty, academic practice. The cohort included 20 control subjects, 60 NPDR patients, and 56 PDR patients.

QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.

Purpose: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.

Methods: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans.

Quantitative optical coherence tomography angiography: A review.

Unlabelled: As a new optical coherence tomography (OCT) modality, OCT angiography (OCTA) provides a noninvasive method to detect microvascular distortions correlated with eye conditions. By providing unparalleled capability to differentiate individual plexus layers in the retina, OCTA has demonstrated its excellence in clinical management of diabetic retinopathy, glaucoma, sickle cell retinopathy, diabetic macular edema, and other eye diseases. Quantitative OCTA analysis of retinal and choroidal vasculatures is essential to standardize objective interpretations of clinical outcome.

OCT Angiography Biomarkers for Predicting Visual Outcomes after Ranibizumab Treatment for Diabetic Macular Edema.

Purpose: To correlate quantitative OCT angiography (OCTA) biomarkers with clinical features and to predict the extent of visual improvement after ranibizumab treatment for diabetic macular edema (DME) with OCTA biomarkers.

Design: Retrospective, longitudinal study in Taiwan.

Participants: Fifty eyes of 50 patients with DME and 22 eyes of 22 healthy persons, with the exception of cataract and refractive error, from 1 hospital.

Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies.

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases.

Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy.

This study is to establish quantitative features of vascular geometry in optical coherence tomography angiography (OCTA) and validate them for the objective classification of diabetic retinopathy (DR). Six geometric features, including total vessel branching angle (VBA: θ), child branching angles (CBAs: α1 and α2), vessel branching coefficient (VBC), and children-to-parent vessel width ratios (VWR1 and VWR2), were automatically derived from each vessel branch in OCTA. Comparative analysis of heathy control, diabetes with no DR (NoDR), and non-proliferative DR (NPDR) was conducted.

Near infrared oximetry-guided artery-vein classification in optical coherence tomography angiography.

Unlabelled: Differential artery–vein analysis is valuable for early detection of diabetic retinopathy and other eye diseases. As a new optical coherence tomography imaging modality, optical coherence tomography angiography provides capillary level resolution for accurate examination of retinal vasculatures. However, differential artery–vein analysis in optical coherence tomography angiography particularly for macular region in which blood vessels are small is challenging.

OCT feature analysis guided artery-vein differentiation in OCTA.

Differential artery-vein analysis promises better sensitivity for retinal disease detection and classification. However, clinical optical coherence tomography angiography (OCTA) instruments lack the function of artery-vein differentiation. This study aims to verify the feasibility of using OCT intensity feature analysis to guide artery-vein differentiation in OCTA.

Differential Artery-Vein Analysis Improves the Performance of OCTA Staging of Sickle Cell Retinopathy.

Purpose: We test if differential artery-vein analysis can increase the performance of optical coherence tomography angiography (OCTA) detection and classification of sickle cell retinopathy (SCR).

Method: This observational case series was conducted in a tertiary-retina practice. Color fundus and OCTA images were collected from 20 control and 48 SCR subjects.

Comparative Optical Coherence Tomography Angiography of Wild-Type and rd10 Mouse Retinas.

Purpose: To conduct longitudinal optical coherence tomography angiography (OCTA) to characterize dynamic changes of trilaminar vascular plexuses in wild-type (WT) and retinal degeneration 10 (rd10) mouse retinas.

Methods: Longitudinal in vivo OCT/OCTA measurements of WT and rd10 mouse retinas were conducted at postnatal day 14 (P14), P17, P21, P24, and P28. OCT images were used to quantify retinal thickness changes, while OCTA images were used to investigate vascular dynamics within the trilaminar vascular plexuses, that is, superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP).

Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography.

Purpose: This study aimed to develop a method for automated artery-vein classification in optical coherence tomography angiography (OCTA), and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and staging of diabetic retinopathy (DR).

Methods: For each patient, the color fundus image was used to guide the artery-vein differentiation in the OCTA image. Traditional mean blood vessel caliber (m-BVC) and mean blood vessel tortuosity (m-BVT) in OCTA images were quantified for control and DR groups.