Retinal Vascular Fractal Dimensions and Their Association with Macrovascular Cardiac Disease

Retinal vessel architecture and geometry are not impaired in normal-tension glaucoma

To investigate the associations between retinal vessel parameters and normal-tension glaucoma (NTG). We conducted a case-control study with a prospective cohort, allowing to record 23 cases of NTG. We matched NTG patient with one primary open-angle glaucoma (POAG) and one control per case by age, systemic hypertension, diabetes, and refraction. Central retinal artery equivalent (CRAE), central retinal venule equivalent (CRVE), Arteriole-To-Venule ratio (AVR), Fractal Dimension and tortuosity of the vascular network were measured using VAMPIRE software. Our sample consisted of 23 NTG, 23 POAG, and 23 control individuals, with a median age of 65 years (25-75th percentile, 56-74). No significant differences were observed in median values for CRAE (130.6 µm (25-75th percentile, 122.8; 137.0) for NTG, 128.4 µm (124.0; 132.9) for POAG, and 135.3 µm (123.3; 144.8) for controls, P = .23), CRVE (172.1 µm (160.0; 188.3), 172.8 µm (163.3; 181.6), and 175.9 µm (167.6; 188.4), P = .43), AVR (0.76, 0.75, 0.74, P = .71), tortuosity and fractal parameters across study groups. Vascular morphological parameters were not significantly associated with retinal nerve fiber layer thickness or mean deviation for the NTG and POAG groups. Our results suggest that vascular dysregulation in NTG does not modify the architecture and geometry of the retinal vessel network.

Retinal Vascular Geometry in Hypertension: cSLO-Based Method

INTRODUCTION

We aim to introduce a method using confocal scanning laser ophthalmoscopy (cSLO) images for measuring retinal vascular geometry, including vessel branch angle (BA), vessel diameter, vessel tortuosity, and fractal dimension (D_f), and to elucidate the relationship between hypertension and these metrics.

METHODS

A total of 119 participants (119 eyes) were enrolled, among which 72 were normotensive and 47 were hypertensive. Infrared cSLO images were extracted from the circular scan around the optics disc using a commercial cSLO + optical coherence tomography instrument. Preprocessed cSLO images were further analyzed using the appropriate tool/macro/plugin of ImageJ.

RESULTS

Intraclass correlation coefficients of selected methods used for conducting the cSLO-based geometric analyses were all higher than 0.80. Arterial/arteriolar BA, arteriolar vessel diameter, and total D_f in normotensive subjects were 85.80 ± 7.79°, 116.80 ± 12.58 μm, and 1.430 ± 0.037, respectively, significantly higher than those of hypertensive subjects (82.13 ± 10.83°, 108.2 ± 11.12 μm, and 1.361 ± 0.044, all P < 0.05). The aforementioned metrics remained negatively correlated with hypertension even after adjusting for age alone or age and gender (P < 0.05). However, the difference between arteriolar tortuosity and all studied venous/venular geometric parameters in both subjects was insignificant (all P > 0.05).

CONCLUSION

Proposed cSLO-based methods for assessing various vascular geometric parameters were highly repeatable and reproducible. Arterial/arteriolar BA, arteriolar vessel diameter, and total D_f were retinal vascular parameters significantly correlated with hypertension in a negative manner.

Progress of Bulbar Conjunctival Microcirculation Alterations in the Diagnosis of Ocular Diseases

Bulbar conjunctival microcirculation is a microvascular system distributed in the translucent bulbar conjunctiva near the corneal limbus. Multiple ocular diseases lead to bulbar conjunctival microcirculation alterations, which means that bulbar conjunctival microcirculation alterations would be potential screening and diagnostic indicators for these ocular diseases. In recent years, with the emergence and application of a variety of noninvasive observation devices for bulbar conjunctiva microcirculation and new image processing technologies, studies that explored the potential of bulbar conjunctival microcirculation alterations in the diagnosis of ocular diseases have been emerging. However, the potential of bulbar conjunctival microcirculation alterations as indicators for ocular diseases has not been exploited to full advantage. The observation devices, image processing methods, and algorithms are not unified. And large-scale research is needed to concrete bulbar conjunctival microcirculation alterations as indicators for ocular diseases. In this paper, we provide an update on the progress of bulbar conjunctival microcirculation alterations in the diagnosis of ocular diseases in recent five years (from January 2017 to March 2022). Relevant ocular diseases include contact lens wearing, dry eye, conjunctival malignant melanoma, conjunctival nevus, and diabetic retinopathy.

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

The assessment of vascular complexity in the lower limbs provides relevant information about peripheral artery occlusive diseases (PAOD), thus fostering improvements both in therapeutic decisions and prognostic estimation. The current clinical practice consists of visually inspecting and evaluating cine-angiograms of the interested region, which is largely operator-dependent. We present here an automatic method for segmenting the vessel tree and compute a quantitative measure, in terms of fractal dimension (FD), of the vascular complexity. The proposed workflow consists of three main steps: (i) conversion of the cine-angiographies to single static images with a broader field of view, (ii) automatic segmentation of the vascular trees, and (iii) calculation and assessment of FD as complexity index. In particular, this work defines (1) a method to reduce the inter-observer variability in judging vascular complexity in cine-angiography images from patients affected by peripheral artery occlusive disease (PAOD), and (2) the use of Fractal Dimension as a metric of shape complexity of vascular tree. The inter-class correlation coefficient (ICC) is computed as inter-observer agreement metric and to account for possible systematic error, that depends on the experience of the raters. The automatic segmentation of vascular tree achieved an Area Under the Curve mean value of $$0.77~\pm ~0.07$$ 0.77 ± 0.07 , with a min-max range of $$0.57-0.87$$ 0.57 - 0.87 . Absolute operator agreement was higher over the segmented image ($$ICC=0.96$$ I C C = 0.96 ) compared to the video ($$ICC=0.76$$ I C C = 0.76 ) and the a broader field of view image ($$ICC=0.92$$ I C C = 0.92 ). Fractal Dimension computed on both manual segmented images (ground truths) and automatically showed a good correlation with the clinical score (0.85 and 0.75, respectively). Experimental analyses suggest that extracting the vascular tree from cine-angiography can substantially improve the reliability of visual assessment of vascular complexity in PAOD. Results also reveal the effectiveness of FD in evaluating complex vascular tree structures.

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

BACKGROUND

The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease.

METHODS

We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases-based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity.

RESULTS

Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling).

CONCLUSIONS

Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.