Analysis of the Retinal and Choroidal Vasculature Using Ultrawidefield Fundus Imaging in Mild Cognitive Impairment and Normal Cognition.

Purpose: To utilize ultrawidefield (UWF) imaging to evaluate retinal and choroidal vasculature and structure in individuals with mild cognitive impairment (MCI) compared with that of controls with normal cognition. Design: Prospective cross sectional study. Participants: One hundred thirty-one eyes of 82 MCI patients and 230 eyes of 133 cognitively normal participants from the Eye Multimodal Imaging in Neurodegenerative Disease Study. Methods: A scanning laser ophthalmoscope (California, Optos Inc) was used to obtain UWF fundus color images. Images were analyzed with the Vasculature Assessment Platform for Images of the Retina UWF (VAMPIRE-UWF 2.0, Universities of Edinburgh and Dundee) software. Main outcome measures: Imaging parameters included vessel width gradient, vessel width intercept, large vessel choroidal vascular density, vessel tortuosity, and vessel fractal dimension. Results: Both retinal artery and vein width gradients were less negative in MCI patients compared with controls, demonstrating decreased rates of vessel thinning at the periphery (P < 0.001; P = 0.027). Retinal artery and vein width intercepts, a metric that extrapolates the width of the vessel at the center of the optic disc, were smaller in MCI patients compared with that of controls (P < 0.001; P = 0.017). The large vessel choroidal vascular density, which quantifies the vascular area versus the total choroidal area, was greater in MCI patients compared with controls (P = 0.025). Conclusions: When compared with controls with normal cognition, MCI patients had thinner retinal vasculature manifested in both the retinal arteries and the veins. In MCI, these thinner arteries and veins attenuated at a lower rate when traveling toward the periphery. MCI patients also had increased choroidal vascular density. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Characterizing differences in retinal and choroidal microvasculature and structure in individuals with Huntington's Disease compared to healthy controls: A cross-sectional prospective study.

OBJECTIVE: To characterize retinal and choroidal microvascular and structural changes in patients who are gene positive for mutant huntingtin protein (mHtt) with symptoms of Huntington's Disease (HD). METHODS: This study is a cross-sectional comparison of patients who are gene positive for mHtt and exhibit symptoms of HD, either motor manifest or prodromal (HD group), and cognitively normal individuals without a family history of HD (control group). HD patients were diagnosed by Duke movement disorder neurologists based on the Unified Huntington's Disease Rating Scale (UHDRS). Fovea and optic nerve centered OCT and OCTA images were captured using Zeiss Cirrus HD-5000 with AngioPlex. Outcome metrics included central subfield thickness (CST), peripapillary retinal nerve fiber layer (pRNFL) thickness, ganglion cell-inner plexiform layer (GCIPL) thickness, and choroidal vascularity index (CVI) on OCT, and foveal avascular zone (FAZ) area, vessel density (VD), perfusion density (PD), capillary perfusion density (CPD), and capillary flux index (CFI) on OCTA. Generalized estimating equation (GEE) models were used to account for inter-eye correlation. RESULTS: Forty-four eyes of 23 patients in the HD group and 77 eyes of 39 patients in the control group were analyzed. Average GCIPL thickness and FAZ area were decreased in the HD group compared to controls (p = 0.001, p < 0.001). No other imaging metrics were significantly different between groups. CONCLUSIONS: Patients in the HD group had decreased GCIPL thickness and smaller FAZ area, highlighting the potential use of retinal biomarkers in detecting neurodegenerative changes in HD.

Differences in Retinal and Choroidal Microvasculature and Structure in Dementia With Lewy Bodies Compared With Normal Cognition.

Purpose: To evaluate the retinal and choroidal microvasculature and structure in individuals with dementia with Lewy bodies (DLB) compared with controls with normal cognition using optical coherence tomography (OCT) and OCT angiography (OCTA). Methods: An institutional review board-approved cross-sectional comparison of patients with DLB and cognitively normal controls was performed. The Cirrus HD-OCT 5000 with AngioPlex (Carl Zeiss Meditec) was used to obtain OCT and OCTA images. Results: Thirty-four eyes of 18 patients with DLB and 85 eyes of 48 cognitively normal patients were analyzed. The average capillary perfusion density (CPD) was higher in the DLB group than in the control group (P = .005). The average capillary flux index (CFI) and ganglion cell inner-plexiform layer (GC-IPL) thickness were lower in the DLB group than in the control group (P = .016 and P = .040, respectively). Conclusions: Patients with DLB had an increased peripapillary CPD, decreased peripapillary CFI, and attenuated GC-IPL thickness compared with those with normal cognition.

Convolutional Neural Network-Based Automated Quality Assessment of OCT and OCT Angiography Image Maps in Individuals With Neurodegenerative Disease.

Purpose: To train and test convolutional neural networks (CNNs) to automate quality assessment of optical coherence tomography (OCT) and OCT angiography (OCTA) images in patients with neurodegenerative disease. Methods: Patients with neurodegenerative disease were enrolled in the Duke Eye Multimodal Imaging in Neurodegenerative Disease Study. Image inputs were ganglion cell-inner plexiform layer (GC-IPL) thickness maps and fovea-centered 6-mm × 6-mm OCTA scans of the superficial capillary plexus (SCP). Two trained graders manually labeled all images for quality (good versus poor). Interrater reliability (IRR) of manual quality assessment was calculated for a subset of each image type. Images were split into train, validation, and test sets in a 70%/15%/15% split. An AlexNet-based CNN was trained using these labels and evaluated with area under the receiver operating characteristic (AUC) and summaries of the confusion matrix. Results: A total of 1465 GC-IPL thickness maps (1217 good and 248 poor quality) and 2689 OCTA scans of the SCP (1797 good and 892 poor quality) served as model inputs. The IRR of quality assessment agreement by two graders was 97% and 90% for the GC-IPL maps and OCTA scans, respectively. The AlexNet-based CNNs trained to assess quality of the GC-IPL images and OCTA scans achieved AUCs of 0.990 and 0.832, respectively. Conclusions: CNNs can be trained to accurately differentiate good- from poor-quality GC-IPL thickness maps and OCTA scans of the macular SCP. Translational Relevance: Since good-quality retinal images are critical for the accurate assessment of microvasculature and structure, incorporating an automated image quality sorter may obviate the need for manual image review.