A fluorescein angiography-based computer-aided algorithm for assessing the retinal vasculature in diabetic retinopathy

ASSOCIATION OF INTRAVENOUS FLUORESCEIN ANGIOGRAPHY AND ADAPTIVE OPTICS IMAGING IN DIABETIC RETINOPATHY: A Prospective Case Series

PURPOSE

To our knowledge, we present the first case series investigating the relationship between adaptive optics (AO) imaging and intravenous fluorescein angiography (IVFA) parameters in patients with diabetic retinopathy.

METHODS

Consecutive patients with diabetic retinopathy older than age 18 years presenting to a single center in Toronto, Canada, from 2020 to 2021 were recruited. Adaptive optics was performed with the RTX1 camera (Imagine Eyes, Orsay, France) at retinal eccentricities of 2° and 4°. Intravenous fluorescein angiography was assessed with the artificial intelligence-based RETICAD system to extract blood flow, perfusion, and blood-retinal-barrier (BRB) permeability at the same retinal locations. Correlations between AO and IVFA parameters were calculated using Pearson's correlation coefficient.

RESULTS

Across nine cases, a significant positive correlation existed between photoreceptor spacing on AO and BRB permeability (r = 0.303, P = 0.027), as well as perfusion (r = 0.272, P = 0.049) on IVFA. When stratified by location, a significant positive correlation between photoreceptor dispersion and both BRB permeability and perfusion (r = 0.770, P = 0.043; r = 0.846, P = 0.034, respectively) was observed. Cone density was also negatively correlated with BRB permeability (r = -0.819, P = 0.046).

CONCLUSION

Photoreceptor spacing on AO was significantly correlated with BRB permeability and perfusion on IVFA in patients with diabetic retinopathy. Future studies with larger sample sizes are needed to understand the relationship between AO and IVFA parameters in diverse patient populations.

Mechanisms of blood-retinal barrier disruption related to intraocular inflammation and malignancy

Blood-retinal barrier (BRB) disruption is a common accompaniment of intermediate, posterior and panuveitis causing leakage into the retina and macular oedema resulting in vision loss. It is much less common in anterior uveitis or in patients with intraocular lymphoma who may have marked signs of intraocular inflammation. New drugs used for chemotherapy (cytarabine, immune checkpoint inhibitors, BRAF inhibitors, EGFR inhibitors, bispecific anti-EGFR inhibitors, MET receptor inhibitors and Bruton tyrosine kinase inhibitors) can also cause different types of uveitis and BRB disruption. As malignant disease itself can cause uveitis, particularly from breast, lung and gastrointestinal tract cancers, it can be clinically difficult to sort out the cause of BRB disruption. Immunosuppression due to malignant disease and/or chemotherapy can lead to infection which can also cause BRB disruption and intraocular infection. In this paper we address the pathophysiology of BRB disruption related to intraocular inflammation and malignancy, methods for estimating the extent and effect of the disruption and examine why some types of intraocular inflammation and malignancy cause BRB disruption and others do not. Understanding this may help sort and manage these patients, as well as devise future therapeutic approaches.

Assessment of a Novel Semi-Automated Algorithm for the Quantification of the Parafoveal Capillary Network

Introduction

We present a novel semi-automated computerized method for the detection and quantification of parafoveal capillary network (PCN) in fluorescein angiography (FA) images.

Material and Methods

An algorithm detecting the superficial parafoveal capillary bed in high-resolution grayscale FA images and creating a one-pixel-wide PCN skeleton was developed using MatLab software. In addition to PCN detection, capillary density and branch point density in two circular areas centered on the center of the foveal avascular zone of 500μm and 750μm radius was calculated by the algorithm. Three consecutive FA images with distinguishable PCN from 56 eyes from 56 subjects were used for analysis. Both manual and semi-automated detection of the PCN and branch points was performed and compared. Three different intensity thresholds were used for the PCN detection to optimize the method defined as mean(I)+0.05*SD(I), mean(I) and mean(I)-0.05*SD(I), where I is the grayscale intensity of each image and SD the standard deviation. Limits of agreement (LoA), intraclass correlation coefficient (ICC) and Pearson's correlation coefficient (r) were calculated.

Results

Using mean(I)-0.05*SD(I) as threshold the average difference in PCN density between semi-automated and manual method was 0.197 (0.316) deg^-1 at 500μm radius and 0.409 (0.562) deg^-1 at 750μm radius. The LoA were -0.421 to 0.817 and -0.693 to 1.510 deg^-1, respectively. The average difference of branch point density between semi-automated and manual method was zero for both areas; LoA were -0.001 to 0.002 and -0.001 to 0.001 branch points/degrees², respectively. The other two intensity thresholds provided wider LoA for both metrics. The semi-automated algorithm showed great repeatability (ICC>0.91 in the 500μm radius and ICC>0.84 in the 750μm radius) for both metrics.

Conclusion

This semi-automated algorithm seems to provide readings in agreement with those of manual capillary tracing in FA. Larger prospective studies are needed to confirm the utility of the algorithm in clinical practice.