PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.

Purpose: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness. Methods: We used deep learning to segment the optic disc, fovea, and vessels in fundus photographs, and measured pallor. We assessed the relationship between pallor and pRNFL thickness derived from optical coherence tomography scans in 118 participants. Separately, we used images diagnosed by clinical inspection as pale (n = 45) and assessed how measurements compared with healthy controls (n = 46). We also developed automatic rejection thresholds and tested the software for robustness to camera type, image format, and resolution. Results: We developed software that automatically quantified disc pallor across several zones in fundus photographs. Pallor was associated with pRNFL thickness globally (ß = -9.81; standard error [SE] = 3.16; P < 0.05), in the temporal inferior zone (ß = -29.78; SE = 8.32; P < 0.01), with the nasal/temporal ratio (ß = 0.88; SE = 0.34; P < 0.05), and in the whole disc (ß = -8.22; SE = 2.92; P < 0.05). Furthermore, pallor was significantly higher in the patient group. Last, we demonstrate the analysis to be robust to camera type, image format, and resolution. Conclusions: We developed software that automatically locates and quantifies disc pallor in fundus photographs and found associations between pallor measurements and pRNFL thickness. Translational Relevance: We think our method will be useful for the identification, monitoring, and progression of diseases characterized by disc pallor and optic atrophy, including glaucoma, compression, and potentially in neurodegenerative disorders.

From Data to Diagnosis: Skin Cancer Image Datasets for Artificial Intelligence.

Artificial Intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep learning architectures, require large digital image datasets for development. This review provides an overview of the datasets used to develop AI algorithms and highlights the importance of dataset transparency for evaluation of algorithm generalisability across varying populations and settings. Current challenges for curation of clinically valuable datasets are detailed, which include dataset shifts arising from demographic variations and differences in data collection methodologies, along with inconsistencies in labelling. These shifts can lead to differential algorithm performance, compromise of clinical utility, and the propagation of discriminatory biases when developed algorithms are implemented in mismatched populations. Limited representation of rare skin cancers and minoritised groups in existing datasets are highlighted which can further skew algorithm performance. Strategies to address these challenges are presented, which include improving transparency, representation and interoperability. Federated learning and generative methods, which may improve dataset size and diversity without compromising privacy, are also examined. Lastly, we discuss model-level techniques which may address biases entrained through the use of datasets derived from routine clinical care. As the role of AI in skin cancer diagnosis becomes more prominent, ensuring the robustness of underlying datasets is increasingly important.

Ultra-Widefield Imaging of the Retinal Macrovasculature in Parkinson Disease Versus Controls With Normal Cognition Using Alpha-Shapes Analysis.

Purpose: To investigate retinal vascular characteristics using ultra-widefield (UWF) scanning laser ophthalmoscopy in Parkinson disease (PD). Methods: Individuals with an expert-confirmed clinical diagnosis of PD and controls with normal cognition without PD underwent Optos California UWF imaging. Patients with diabetes, uncontrolled hypertension, glaucoma, dementia, other movement disorders, or known retinal or optic nerve pathology were excluded. Images were analyzed using Vasculature Assessment and Measurement Platform for Images of the Retina (VAMPIRE-UWF) software, which describes retinal vessel width gradient and tortuosity, provides vascular network fractal dimensions, and conducts alpha-shape analysis to further characterize vascular morphology (complexity, Opαmin; spread, OpA). Results: In the PD cohort, 53 eyes of 38 subjects were assessed; in the control cohort, 51 eyes of 33 subjects were assessed. Eyes with PD had more tortuous retinal arteries in the superotemporal quadrant (P = 0.043). In eyes with PD, alpha-shape analysis revealed decreased OpA, indicating less retinal vasculature spread compared to controls (P = 0.032). Opαmin was decreased in PD (P = 0.044), suggesting increased vascular network complexity. No differences were observed in fractal dimension in any region of interest. Conclusions: This pilot study suggests that retinal vasculature assessment on UWF images using alpha-shape analysis reveals differences in retinal vascular network spread and complexity in PD and may be a more sensitive metric compared to fractal dimension. Translational Relevance: Retinal vasculature assessment using these novel methods may be useful in understanding ocular manifestations of PD and the development of retinal biomarkers.

The Scottish Medical Imaging Archive: 57.3 Million Radiology Studies Linked to Their Medical Records.

Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

Classifying Real-world Macroscopic Images in the Primary-Secondary Care Interface using Transfer Learning: Implications for Development of Artificial Intelligence Solutions using Non- dermoscopic Images.

BACKGROUND: Application of deep learning to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability. Further research is necessary to determine the generalisability of deep learning algorithms across populations and acquisition settings. OBJECTIVES: We assessed the extent to which deep learning can generalise to non-dermoscopic datasets acquired at the primary-secondary care interface in the National Health Service (NHS). We explored how to obtain clinically satisfactory performance on non-standardised, real-world local data without availability of large diagnostically labelled local datasets. We measured the impact of pre-training deep learning algorithms on external, public-domain datasets. METHODS: Diagnostic macroscopic image datasets were created from previous referrals from primary to secondary care. These included 2213 images referred from primary care practitioners in NHS Tayside and 1510 images from NHS Forth Valley acquired by medical photographers. Two further datasets with identical diagnostic labels were obtained from public domain sources, namely the International Skin Imaging Collaboration (ISIC) dermoscopic dataset and the SD-260 non-dermoscopic dataset. Deep learning algorithms, specifically SWIN transformers and an EfficientNets, were trained using data from each of these datasets. Algorithms were also fine-tuned on images from the NHS datasets after pre-training on different data combinations, including the larger public domain datasets. ROC curves and area under such curves (AUC) were used to assess performance. RESULTS: SWIN transformers tested on Forth Valley data had AUCs of 0.85 and 0.89 when trained on SD-260 and Forth Valley data, respectively. Training on SD-260 followed by fine-tuning of Forth Valley data gave an AUC of 0.91. Similar effects of pre-training and tuning on local data were observed using Tayside data, and EfficientNets. Pre-training on the larger dermoscopic image dataset (ISIC-2019) provided no additional benefit. CONCLUSIONS: Pre-training on public macroscopic images, followed by tuning to local data, gave promising results. Further improvements are needed to afford deployment in real clinical pathways. Larger datasets local to the target domain might be expected to yield further improved performance.

Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks.

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community.

Self-supervised monocular depth estimation for high field of view colonoscopy cameras.

Optical colonoscopy is the gold standard procedure to detect colorectal cancer, the fourth most common cancer in the United Kingdom. Up to 22%-28% of polyps can be missed during the procedure that is associated with interval cancer. A vision-based autonomous soft endorobot for colonoscopy can drastically improve the accuracy of the procedure by inspecting the colon more systematically with reduced discomfort. A three-dimensional understanding of the environment is essential for robot navigation and can also improve the adenoma detection rate. Monocular depth estimation with deep learning methods has progressed substantially, but collecting ground-truth depth maps remains a challenge as no 3D camera can be fitted to a standard colonoscope. This work addresses this issue by using a self-supervised monocular depth estimation model that directly learns depth from video sequences with view synthesis. In addition, our model accommodates wide field-of-view cameras typically used in colonoscopy and specific challenges such as deformable surfaces, specular lighting, non-Lambertian surfaces, and high occlusion. We performed qualitative analysis on a synthetic data set, a quantitative examination of the colonoscopy training model, and real colonoscopy videos in near real-time.

Effects of pupil dilation with topical 0.5% tropicamide on retinal vascular parameters assessed by VAMPIRE® software in healthy cats.

Our study investigates the effects of mydriasis obtained with topical 0.5% tropicamide on retinal vascular parameters evaluated in cats using the retinal imaging software: Vascular Assessment and Measurement Platform for Images of the Retina (VAMPIRE®). Forty client-owned healthy adult cats were included in the study. Topical 0.5% tropicamide was applied to dilate only the right pupil. The left eye was used as a control. Before dilation (T0), infrared pupillometry of both pupils was performed and fundus oculi images were taken from both eyes. Right eye fundus images were then captured 30 min after topical application of tropicamide (T30), when mydriasis was achieved. The retinal vessel widths (3 arteries and 3 veins) were measured with VAMPIRE® in four standard measurement areas (SMA) identified with the letters A, B, C, D. Average value of the 3 vessel widths was used. After normality assessment, the t-test was used to analyse the mean difference in vascular parameters of the left and right eyes at T0 and T30, with p set <0.05. The two eyes showed no statistical differences in pupil and vascular parameter measurements at T0. At T30, only one artery measurement of the right eye (SMA A-peripapillary area) showed a small but statistically significant mean vasoconstriction of approximately 4%. The results indicate that local application of 0.5% tropicamide seems to be associated with a small retinal arteriolar vasoconstriction as assessed by VAMPIRE® in cats. However, this change is minimal, and should not affect the interpretation of the results when VAMPIRE® is used.

Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes.

There is increasing evidence that the complexity of the retinal vasculature measured as fractal dimension, Df, might offer earlier insights into the progression of coronary artery disease (CAD) before traditional biomarkers can be detected. This association could be partly explained by a common genetic basis; however, the genetic component of Df is poorly understood. We present a genome-wide association study (GWAS) of 38,000 individuals with white British ancestry from the UK Biobank aimed to comprehensively study the genetic component of Df and analyse its relationship with CAD. We replicated 5 Df loci and found 4 additional loci with suggestive significance (P < 1e-05) to contribute to Df variation, which previously were reported in retinal tortuosity and complexity, hypertension, and CAD studies. Significant negative genetic correlation estimates support the inverse relationship between Df and CAD, and between Df and myocardial infarction (MI), one of CAD's fatal outcomes. Fine-mapping of Df loci revealed Notch signalling regulatory variants supporting a shared mechanism with MI outcomes. We developed a predictive model for MI incident cases, recorded over a 10-year period following clinical and ophthalmic evaluation, combining clinical information, Df, and a CAD polygenic risk score. Internal cross-validation demonstrated a considerable improvement in the area under the curve (AUC) of our predictive model (AUC = 0.770 ± 0.001) when comparing with an established risk model, SCORE, (AUC = 0.741 ± 0.002) and extensions thereof leveraging the PRS (AUC = 0.728 ± 0.001). This evidences that Df provides risk information beyond demographic, lifestyle, and genetic risk factors. Our findings shed new light on the genetic basis of Df, unveiling a common control with MI, and highlighting the benefits of its application in individualised MI risk prediction.

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.

Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept.

Cortical bone microstructure assessment in biological and forensic anthropology can assist with the estimation of age-at-death and animal-human differentiation, for example. Osteonal structures within cortical bone are the key feature under analysis, with osteon frequency and metric parameters providing crucial information for the assessment. Currently, the histomorphological assessment consists of a time-consuming manual process for which specific training is required. Our work investigates the feasibility of automatic analysis of human bone microstructure images through the application of deep learning. In this paper, we use a U-Net architecture to address the semantic segmentation of such images into three classes: intact osteons, fragmentary osteons, and background. Data augmentation was used to avoid overfitting. We evaluated our fully automatic approach using a sample of 99 microphotographs. The contours of intact and fragmentary osteons were traced manually to provide ground truth. The Dice coefficients were 0.73 for intact osteons, 0.38 for fragmented osteons, and 0.81 for background, giving an average of 0.64. The Dice coefficient of the binary classification osteon-background was 0.82. Although further refinement of the initial model and tests with larger datasets are needed, this study provides, to the best of our knowledge, the first proof of concept for the use of computer vision and deep learning for differentiating both intact and fragmentary osteons in human cortical bone. This approach has the potential to widen and facilitate the use of histomorphological assessment in the biological and forensic anthropology communities.

Feasibility of Automated Gonioscopy Imaging in Clinical Practice.

PRCIS: Automated gonioscopy provided good-quality images of the anterior chamber angle. There was a short learning curve for operators, and the examination was well tolerated by patients. Patients expressed a preference for automated gonioscopy compared with traditional gonioscopy. PURPOSE: The purpose of this study was to assess the feasibility of using a desktop automated gonioscopy camera in glaucoma clinics by examining patient tolerability, ease of use, and image quality and comparing patient preference compared with traditional gonioscopy. PATIENTS AND METHODS: A prospective study was conducted in a university hospital clinic. Traditional gonioscopy was performed followed by imaging of the iridocorneal angle (ICA) using the Nidek GS-1 camera by 2 glaucoma specialists. Participants were asked to rate the comfort of automated gonioscopy and which method they preferred. The clinicians graded the ease of acquisition for each patient, and the image quality was reviewed by a grader. RESULTS: Forty-three eyes of 25 participants were included. Sixty-eight percent of participants viewed automated gonioscopy as "extremely comfortable," and the remainder reported it "comfortable". Forty percent preferred automated gonioscopy compared with traditional gonioscopy, while 52% were equivocal. Clinicians scored 32% of participants as "somewhat difficult" to the image. In 46% of eyes, good-quality photographs were obtained for 360 degrees of the ICA. Only 1 eye had no parts of the ICA clearly visible. Seventy-four percent of eyes had at least half of the ICA clearly visible in all 4 quadrants. CONCLUSION: Automated gonioscopy provided good-quality images of the ICA for most patients. It was often not possible to image the entire 360 degrees at the first attempt, but the examination was comfortable for patients, and only 8% preferred traditional gonioscopy to the automated photographic examination.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia.

Importance: The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts. Objective: To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population. Design, Setting, and Participants: This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022. Main Outcomes and Measures: Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers. Results: A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 µm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 µm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 µm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes. Conclusions and Relevance: In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

The persistence of knuckle creases during finger flexion for the identification of perpetrators from digital images of their hands.

The analysis of knuckle creases is part of the multifactorial assessment of digital images of the hand used to assist in the identification of perpetrators captured in images depicting child sexual abuse and other offending behaviours. To quantify the impact of finger flexion on the appearance of the dorsal knuckle creases associated with the proximal interphalangeal joint (PIP) joint in digital images, the collection of knuckle crease images, at different points of flexion, was facilitated through an app-based Citizen Science project, Knuckle Down ID. A method of knuckle crease classification was adapted to assess the images collected and was used to assess the impact of finger flexion on the frequency of different knuckle crease features observed in manual analysis. The results show that the adapted methodology had good intra-observer repeatability when the entire method was assessed (ICC.81). Specifically, the recording of the knuckle crease features had good inter-observer reliability (ICC.99) whereas the tracing aspect of the methodology was less repeatable (DSC of whole trace.24). The impact of flexion on the recorded frequency of knuckle creases was shown to be significant (p = .00). However, when comparing two hands at different points of flexion, only when comparing fingers held at 0° vs 45° (p = .04) as well as fingers held at 90° vs any other finger position was the differences in the knuckle crease feature frequencies significant (p = .00). The comparison of knuckle crease image pairs at 20° of hyperflexion vs 0° (p = .10) and 20° of hyperflexion vs 45° (p = .09) showed that the difference in knuckle crease feature frequencies was not significant. This has implications for examiners and improves understanding of the limitations of the 1:1 comparison of knuckle creases in casework evidence in which perpetrator's hands are unconstrained.

Assessment of Indices of Conjunctival Microvascular Function in Patients With and Without Obstructive Coronary Artery Disease.

BACKGROUND: Atherosclerotic heart disease often remains asymptomatic until presentation with a major adverse cardiovascular event. Primary preventive therapies improve outcomes, but conventional screening often misattributes risk. Vascular imaging can be utilised to detect atherosclerosis, but often involves ionising radiation. The conjunctiva is a readily accessible vascular network allowing non-invasive hemodynamic evaluation. AIM: To compare conjunctival microcirculatory function in patients with and without obstructive coronary artery disease. METHODS: We compared the conjunctival microcirculation of myocardial infarction patients (MI-cohort) to controls with no obstructive coronary artery disease (NO-CAD cohort). Conjunctival imaging was performed using a smartphone and slit-lamp biomicroscope combination. Microvascular indices of axial (Va) and cross-sectional (Vcs) velocity; blood flow rate (Q); and wall shear rate (WSR) were compared in all conjunctival vessels between 5 and 45 µm in diameter. RESULTS: A total of 127 patients were recruited (66 MI vs 61 NO-CAD) and 3602 conjunctival vessels analysed (2414 MI vs 1188 NO-CAD). Mean Va, Vcs and Q were significantly lower in the MI vs NO-CAD cohort (Va 0.50 ± 0.17 mm/s vs 0.55 ± 0.15 mm/s, p < 0.001; Vcs 0.35 ± 0.12 mm/s vs 0.38 ± 0.10 mm/s, p < 0.001; Q 154 ± 116 pl/s vs 198 ± 130 pl/s, p < 0.001). To correct for differences in mean vessel diameter, WSR was compared in 10-36 µm vessels (3268/3602 vessels) and was lower in the MI-cohort (134 ± 64 s-1 vs 140 ± 63 s-1, p = 0.002). CONCLUSIONS: Conjunctival microcirculatory alterations can be observed in patients with obstructive coronary artery disease. The conjunctival microvasculature merits further evaluation in cardiovascular risk screening.

Retinal Vascular Changes in Alzheimer's Dementia and Mild Cognitive Impairment: A Pilot Study Using Ultra-Widefield Imaging.

Purpose: Retinal microvascular abnormalities measured on retinal images are a potential source of prognostic biomarkers of vascular changes in the neurodegenerating brain. We assessed the presence of these abnormalities in Alzheimer's dementia and mild cognitive impairment (MCI) using ultra-widefield (UWF) retinal imaging. Methods: UWF images from 103 participants (28 with Alzheimer's dementia, 30 with MCI, and 45 with normal cognition) underwent analysis to quantify measures of retinal vascular branching complexity, width, and tortuosity. Results: Participants with Alzheimer's dementia displayed increased vessel branching in the midperipheral retina and increased arteriolar thinning. Participants with MCI displayed increased rates of arteriolar and venular thinning and a trend for decreased vessel branching. Conclusions: Statistically significant differences in the retinal vasculature in peripheral regions of the retina were observed among the distinct cognitive stages. However, larger studies are required to establish the clinical importance of our findings. UWF imaging may be a promising modality to assess a larger view of the retinal vasculature to uncover retinal changes in Alzheimer's disease. Translational Relevance: This pilot work reports an investigation into which retinal vasculature measurements may be useful surrogate measures of cognitive decline, as well as technical developments (e.g., measurement standardization), that are first required to establish their recommended use and translational potential.

Lower fractal dimension of retinal vessel for patients with Birdshot chorioretinopathy.

PURPOSE: To identify the retinal vessel vasculature parameters associated with birdshot chorioretinopathy (BSCR). METHODS: This retrospective observational study included 28 prevalent cases of BSCR with a median time from diagnosis of 6 years and 28 controls matched for age, arterial hypertension, diabetes and refraction. Forty-five-degree fundus images of both dilated eyes were acquired with a fundus camera (Canon CR-2, Tokyo, Japan). The summary diameter of the arterial retinal vessels (central retinal artery equivalent, CRAE), venous retinal vessels (central retinal vein equivalent, CRVE), vascular tortuosity and fractal dimension (FD) were measured using VAMPIRE software. Retinal vasculitis was characterized using fluorescein angiography and active choroiditis using indocyanine green angiography. RESULTS: At baseline, BSCR was associated with lower FD compared with matched controls (mean difference, -0.04; 95% confidence interval [CI], -0.06 to -0.02, p < 0.001). No other VAMPIRE parameters (CRAE, CRVE, arterial and venous tortuosity) differed. Among BSCR patients, retinal vein vasculitis was associated with higher CRAE (mean difference, 21 µ; 95% CI, 2.6-40, p = 0.03), venous tortuosity (geometric mean ratio, 1.79; 95% CI, 1.18-2.72, p = 0.007) and FD (mean difference, -0.04; 95% CI, -0.06 to -0.01, p = 0.007). Resolution of retinal vein vasculitis during follow-up was paralleled by decrease in CRAE, CRVE and venous tortuosity values and increase in venous FD, respectively. CONCLUSION: BSCR is associated with lower FD value, suggesting that chronic retinal inflammation induces microvascular remodelling. Efficient treatment of retinal vasculitis may reverse changes in retinal vascular parameters. Changes in retinal vascular parameters could be potentially useful for assessing patients with BSCR disease.