Optical Coherence Tomography Angiography (OCTA) of the eye: A review on basic principles, advantages, disadvantages and device specifications

Advances in retinal imaging biomarkers for the diagnosis of cerebrovascular disease

The increasing incidence and mortality rates of cerebrovascular disease impose a heavy burden on both patients and society. Retinal imaging techniques, such as fundus photography, optical coherence tomography, and optical coherence tomography angiography, can be used for rapid, non-invasive evaluation of cerebral microcirculation and brain function since the retina and the central nervous system share similar embryonic origin characteristics and physiological features. This article aimed to review retinal imaging biomarkers related to cerebrovascular diseases and their applications in cerebrovascular diseases (stroke, cerebral small vessel disease [CSVD], and vascular cognitive impairment [VCI]), thus providing reference for early diagnosis and prevention of cerebrovascular diseases.

A Blood Supply Pathophysiological Microcirculatory Mechanism for Long COVID

BACKGROUND

The term "Long COVID" is commonly used to describe persisting symptoms after acute COVID-19. Until now, proposed mechanisms for the explanation of Long COVID have not related quantitative measurements to basic laws. In this work, a common framework for the Long COVID pathophysiological mechanism is presented, based on the blood supply deprivation and the flow diffusion equation.

METHODS

Case-control studies with statistically significant differences between cases (post-COVID patients) and controls, from multiple tissues and geographical areas, were gathered and tabulated. Microvascular loss (ML) was quantified by vessel density reduction (VDR), foveal avascular zone enlargement (FAZE), capillary density reduction (CDR), and percentage of perfused vessel reduction (PPVR). Both ML and hemodynamic decrease (HD) were incorporated in the tissue blood supply reduction (SR) estimation.

RESULTS

ML data were found from 763 post-COVID patients with an average VDR, FAZE, CDR, and PPVR of 16%, 31%, 14%, and 21%, respectively. The average HD from 72 post-COVID patients was 37%. The estimated SR for multiple tissues with data from 634 post-COVID patients reached a sizeable 47%. This large SR creates conditions of lower mass diffusion rates, hypoxia, and undernutrition, which at a multi-tissue level, for a long time, can explain the wide variety of the Long COVID symptoms.

CONCLUSIONS

Disruption of peripheral tissue blood supply by the contribution of both ML and HD is proposed here to be the principal cause of the mechanism leading to Long COVID symptoms.

Unlocking the Potential of Vessel Density and the Foveal Avascular Zone in Optical Coherence Tomography Angiography as Biomarkers in Alzheimer's Disease

Alzheimer's disease is the most prevalent form of dementia. Apart from its traditional clinical diagnostic methods, novel ocular imaging biomarkers have the potential to significantly enhance the diagnosis of Alzheimer's disease. Ophthalmologists might be able to play a crucial role in this multidisciplinary approach, aiding in the early detection and diagnosis of Alzheimer's disease through the use of advanced retinal imaging techniques. This systematic literature review the utilization of optical coherence tomography angiography biomarkers, specifically vessel density and the foveal avascular zone, for the diagnosis of Alzheimer's disease. A comprehensive search was performed across multiple academic journal databases, including 11 relevant studies. The selected studies underwent thorough analysis to assess the potential of these optical coherence tomography angiography biomarkers as diagnostic tools for Alzheimer's disease. The assessment of vessel density and the foveal avascular zone have emerged as a promising avenue for identifying and diagnosing Alzheimer's disease. However, it is imperative to acknowledge that further targeted investigations are warranted to address the inherent limitations of the existing body of literature. These limitations encompass various factors such as modest sample sizes, heterogeneity among study populations, disparities in optical coherence tomography angiography imaging protocols, and inconsistencies in the reported findings. In order to establish the clinical utility and robustness of these biomarkers in Alzheimer's disease diagnosis, future research endeavors should strive to overcome these limitations by implementing larger-scale studies characterized by standardized protocols and comprehensive assessments.

Progress in clinical research and applications of retinal vessel quantification technology based on fundus imaging

Retinal blood vessels are the only directly observed blood vessels in the body; changes in them can help effective assess the occurrence and development of ocular and systemic diseases. The specificity and efficiency of retinal vessel quantification technology has improved with the advancement of retinal imaging technologies and artificial intelligence (AI) algorithms; it has garnered attention in clinical research and applications for the diagnosis and treatment of common eye and related systemic diseases. A few articles have reviewed this topic; however, a summary of recent research progress in the field is still needed. This article aimed to provide a comprehensive review of the research and applications of retinal vessel quantification technology in ocular and systemic diseases, which could update clinicians and researchers on the recent progress in this field.

The application of functional imaging in visual field defects: a brief review

Visual field defects (VFDs) represent a prevalent complication stemming from neurological and ophthalmic conditions. A range of factors, including tumors, brain surgery, glaucoma, and other disorders, can induce varying degrees of VFDs, significantly impacting patients' quality of life. Over recent decades, functional imaging has emerged as a pivotal field, employing imaging technology to illustrate functional changes within tissues and organs. As functional imaging continues to advance, its integration into various clinical aspects of VFDs has substantially enhanced the diagnostic, therapeutic, and management capabilities of healthcare professionals. Notably, prominent imaging techniques such as DTI, OCT, and MRI have garnered widespread adoption, yet they possess unique applications and considerations. This comprehensive review aims to meticulously examine the application and evolution of functional imaging in the context of VFDs. Our objective is to furnish neurologists and ophthalmologists with a systematic and comprehensive comprehension of this critical subject matter.

Translation of Color Fundus Photography into Fluorescein Angiography Using Deep Learning for Enhanced Diabetic Retinopathy Screening

Purpose

To develop and validate a deep learning model that can transform color fundus (CF) photography into corresponding venous and late-phase fundus fluorescein angiography (FFA) images.

Design

Cross-sectional study.

Participants

We included 51 370 CF-venous FFA pairs and 14 644 CF-late FFA pairs from 4438 patients for model development. External testing involved 50 eyes with CF-FFA pairs and 2 public datasets for diabetic retinopathy (DR) classification, with 86 952 CF from EyePACs, and 1744 CF from MESSIDOR2.

Methods

We trained a deep-learning model to transform CF into corresponding venous and late-phase FFA images. The translated FFA images' quality was evaluated quantitatively on the internal test set and subjectively on 100 eyes with CF-FFA paired images (50 from external), based on the realisticity of the global image, anatomical landmarks (macula, optic disc, and vessels), and lesions. Moreover, we validated the clinical utility of the translated FFA for classifying 5-class DR and diabetic macular edema (DME) in the EyePACs and MESSIDOR2 datasets.

Main Outcome Measures

Image generation was quantitatively assessed by structural similarity measures (SSIM), and subjectively by 2 clinical experts on a 5-point scale (1 refers real FFA); intragrader agreement was assessed by kappa. The DR classification accuracy was assessed by area under the receiver operating characteristic curve.

Results

The SSIM of the translated FFA images were > 0.6, and the subjective quality scores ranged from 1.37 to 2.60. Both experts reported similar quality scores with substantial agreement (all kappas > 0.8). Adding the generated FFA on top of CF improved DR classification in the EyePACs and MESSIDOR2 datasets, with the area under the receiver operating characteristic curve increased from 0.912 to 0.939 on the EyePACs dataset and from 0.952 to 0.972 on the MESSIDOR2 dataset. The DME area under the receiver operating characteristic curve also increased from 0.927 to 0.974 in the MESSIDOR2 dataset.

Conclusions

Our CF-to-FFA framework produced realistic FFA images. Moreover, adding the translated FFA images on top of CF improved the accuracy of DR screening. These results suggest that CF-to-FFA translation could be used as a surrogate method when FFA examination is not feasible and as a simple add-on to improve DR screening.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A normative blood velocity model in the exchange microvessels for discriminating health from disease: Healthy controls versus COVID-19 cases

A usual practice in medicine is to search for "biomarkers" which are measurable quantities of a normal or abnormal biological process. Biomarkers can be biochemical or physical quantities of the body and although commonly used statistically in clinical settings, it is not usual for them to be connected to basic physiological models or equations. In this work, a normative blood velocity model framework for the exchange microvessels was introduced, combining the velocity-diffusion (V-J) equation and statistics, in order to define the normative range (NR) and normative area (NA) diagrams for discriminating normal (normemic) from abnormal (hyperemic or underemic) states, taking into account the microvessel diameter D. This is different from the usual statistical processing since there is a basis on the well-known physiological principle of the flow diffusion equation. The discriminative power of the average axial velocity model was successfully tested using a group of healthy individuals (Control Group) and a group of post COVID-19 patients (COVID-19 Group).

The velocity-diffusion equation in the exchange microvessels

In human and animal microvascular networks, the exchange microvessels are the capillaries and postcapillary venules where material transport between the circulating blood and tissue takes place. For small-size molecules, this material transport is done by the physical mechanism of diffusion through the endothelium wall and the diffusion rate J in relation to blood volume flow Q is described by the flow-diffusion (Q-J) equation. However, the volume flow is not easy to be measured in vivo. The objective of this work was to transform the classical flow-diffusion equation into a new form with axial velocity V as an independent variable instead of volume flow Q. The new form was called the velocity-diffusion (V-J) equation and has the advantage that V can be measured directly in vivo by optical imaging techniques. The V-J equation could have important applications in the calculation of the mass diffusion rate of various substances in vivo.