UNASSIGNED: To assess the intrasession repeatability of macular OCT angiography (OCTA) parameters in Alzheimer\'s disease (AD), mild cognitive impairment (MCI), Parkinson\'s disease (PD), and normal cognition (NC).
UNASSIGNED: Cross sectional study.
UNASSIGNED: Patients with a clinical diagnosis of AD, PD, MCI, or NC were imaged. Images with poor quality and of those with diabetes mellitus, glaucoma, or vitreoretinal disease were excluded from analysis.
UNASSIGNED: All participants were imaged using the Zeiss Cirrus HD-5000 with AngioPlex (Carl Zeiss Meditec, Software Version 11.0.0.29946) and repeat OCTA images were obtained for both eyes. Perfusion density (PFD), vessel density (VD), and Foveal avascular zone (FAZ) area were measured from 3 × 3 mm and 6 × 6 mm OCTA images centered on the fovea using an ETDRS grid overlay.
UNASSIGNED: Intraclass correlation coefficients were used to quantify repeatability of PFD, VD, and FAZ area measurements obtained from imaging.
UNASSIGNED: 3 × 3 mm scans of 22 AD, 40 MCI, 21 PD, and 26 NC participants and 6 × 6 mm scans of 29 AD, 44 MCI, 29 PD, and 30 NC participants were analyzed. Repeatability values ranged from 0.64 (0.49-0.82) for 6 × 6 mm PFD in AD participants to 0.87 (0.67-0.92) for 3 × 3 mm PFD in AD participants. No significant differences were observed in repeatability between NC participants and those with neurodegenerative disease.
UNASSIGNED: Overall, similar OCTA repeatability was observed between NC participants and those with neurodegeneration. Regardless of diagnostic group, macular OCTA metrics demonstrated moderate to good repeatability.
UNASSIGNED: The authors have no proprietary or commercial interest in any materials discussed in this article.

UNASSIGNED: Geometric perfusion deficit (GPD) is a newly described OCT angiography (OCTA) parameter identifying the total area of presumed retinal ischemia. The aim of our study is to characterize differences in GPD and other common quantitative OCTA parameters between macular full field, perivenular zones, and periarteriolar zones for each clinical stage of nonproliferative diabetic retinopathy (DR) and to assess the influence of ultrahigh-speed acquisition and averaging on the described differences.
UNASSIGNED: Prospective observational study.
UNASSIGNED: Forty-nine patients, including 11 (22.4%) with no sign of DR, 12 (24.5%) with mild DR, 13 (26.5%) with moderate DR, and 13 (26.5%) with severe DR. Patients with diabetic macular edema, proliferative DR, media opacity, head tremor, and overlapping retinal diseases or systemic diseases influencing OCTA were excluded.
UNASSIGNED: OCT angiography was performed 3 times for each patient: 1 using Solix Fullrange single volume (V1) mode, 1 using Solix Fullrange 4 volumes mode with automatically averaged scan (V4), and 1 using AngioVue.
UNASSIGNED: Full macular, periarteriolar, and perivenular perfusion density (PD), vessel length density (VLD), vessel density index, and GPD for both the superficial capillary plexus (SCP) and deep capillary plexus (DCP).
UNASSIGNED: In patients showing no sign of DR, PD and VLD were significantly lower in the perivenular area in both the DCP and SCP using V1 and V4, whereas GPD was significantly higher in the perivenular zone in the DCP and SCP with all 3 devices. In patients with mild DR, all 3 measurements (PD, VLD, and GPD) were significantly different in the perivenular zone with all 3 devices. In patients with moderate DR, PD and VLD were lower in the DCP and SCP when measured with V1 and V4. Moreover, GPD was higher in the perivenular zone in the DCP with all 3 devices, whereas only V4 detected a difference in the SCP. In severe DR, only V4 detected a lower PD and VLD and a higher GPD in the DCP of the perivenular zone. V4 also detected a higher GPD in the SCP.
UNASSIGNED: Geometric perfusion deficit highlights prevalent perivenular location of macular capillary ischemia in all stages of DR. In severe DR patients, only averaging technology allows detection of the same finding.
UNASSIGNED: The author(s) have no proprietary or commercial interest in any materials discussed in this article.

UNASSIGNED: To develop a severity classification for macular telangiectasia type 2 (MacTel) disease using multimodal imaging.
UNASSIGNED: An algorithm was used on data from a prospective natural history study of MacTel for classification development.
UNASSIGNED: A total of 1733 participants enrolled in an international natural history study of MacTel.
UNASSIGNED: The Classification and Regression Trees (CART), a predictive nonparametric algorithm used in machine learning, analyzed the features of the multimodal imaging important for the development of a classification, including reading center gradings of the following digital images: stereoscopic color and red-free fundus photographs, fluorescein angiographic images, fundus autofluorescence images, and spectral-domain (SD)-OCT images. Regression models that used least square method created a decision tree using features of the ocular images into different categories of disease severity.
UNASSIGNED: The primary target of interest for the algorithm development by CART was the change in best-corrected visual acuity (BCVA) at baseline for the right and left eyes. These analyses using the algorithm were repeated for the BCVA obtained at the last study visit of the natural history study for the right and left eyes.
UNASSIGNED: The CART analyses demonstrated 3 important features from the multimodal imaging for the classification: OCT hyper-reflectivity, pigment, and ellipsoid zone loss. By combining these 3 features (as absent, present, noncentral involvement, and central involvement of the macula), a 7-step scale was created, ranging from excellent to poor visual acuity. At grade 0, 3 features are not present. At the most severe grade, pigment and exudative neovascularization are present. To further validate the classification, using the Generalized Estimating Equation regression models, analyses for the annual relative risk of progression over a period of 5 years for vision loss and for progression along the scale were performed.
UNASSIGNED: This analysis using the data from current imaging modalities in participants followed in the MacTel natural history study informed a classification for MacTel disease severity featuring variables from SD-OCT. This classification is designed to provide better communications to other clinicians, researchers, and patients.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

UNASSIGNED: To identify the vascular biomarkers of peripheral capillary nonperfusion in patients affected by naive central retinal vein occlusion (CRVO), and to analyze their changes over the follow-up.
UNASSIGNED: Consecutive prospective case series with a planned follow-up of 2 years.
UNASSIGNED: Thirty-five patients affected by CRVO and 35 healthy gender- and age-matched subjects were enrolled in the study.
UNASSIGNED: Ophthalmic examination included best corrected visual acuity (BCVA), ultrawidefield fluorescein angiography (UWFFA), OCT, and OCT angiography (OCTA).
UNASSIGNED: Vessel density (VD) at the superficial capillary plexus and deep capillary plexus (DCP) were calculated on OCTA images. The ischemic index (ISI) was calculated on UWFFA.
UNASSIGNED: The mean baseline ISI was 37%, increasing to 40% at the end of the follow-up, whereas it was 4.9% in the patients\' fellow eyes and 4.5% in the control group with no change over the follow-up. OCT angiography revealed VD reduction in the DCP, considering both 3 × 3 mm and 12 × 12 mm scans. The correlation analyses revealed that DCP VD was the only parameter showing a statistically significant correlation with the foveal avascular zone (FAZ) area, BCVA, and ISI.
UNASSIGNED: Deep capillary plexus VD impairment is detectable in all CRVO cases, variably involving both the central retina (with enlarged FAZ) and the periphery (with VD reduction in the peripheral retina). The severity of DCP VD reduction has correlates with various clinical markers. Deep capillary plexus VD may represent a crucial biomarker to characterize CRVO, and further studies are necessary to identify the cutoff thresholds for the different clinical manifestations.

UNASSIGNED: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.
UNASSIGNED: Cross sectional study.
UNASSIGNED: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.
UNASSIGNED: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.
UNASSIGNED: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.
UNASSIGNED: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.
UNASSIGNED: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

UNASSIGNED: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.
UNASSIGNED: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).
UNASSIGNED: Patients with type 1 DM and controls included in the progenitor study.
UNASSIGNED: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.
UNASSIGNED: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.
UNASSIGNED: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.
UNASSIGNED: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

UNASSIGNED: To investigate the distribution of clinically significant nonperfusion areas (NPAs) on widefield OCT angiography (OCTA) images in patients with diabetes.
UNASSIGNED: Prospective, cross-sectional, observational study.
UNASSIGNED: One hundred and forty-four eyes of 114 patients with diabetes.
UNASSIGNED: Nominal 20 × 23 mm OCTA images were obtained using a swept-source OCTA device (Xephilio OCT-S1), followed by the creation of en face images 20-mm (1614 pixels) in diameter centering on the fovea. The nonperfusion squares (NPSs) were defined as the 10 × 10 pixel squares without retinal vessels, and the ratio of eyes with the NPSs to all eyes in each square was referred to as the NPS ratio. The areas with probabilistic differences (APD) for proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) (APD[PDR] and APD[NPDR]) were defined as sets of squares with higher NPS ratios in eyes with PDR and NPDR, respectively. The P ratio (NPSs within APD[PDR] but not APD[NPDR]/all NPSs) was also calculated.
UNASSIGNED: The probabilistic distribution of the NPSs and the association with diabetic retinopathy (DR) severity.
UNASSIGNED: The NPSs developed randomly in eyes with mild and moderate NPDR and were more prevalent in the extramacular areas and the temporal quadrant in eyes with severe NPDR and PDR. The APD(PDR) was distributed mainly in the extramacular areas, sparing the areas around the vascular arcades and radially peripapillary capillaries. The APD(PDR) contained retinal neovascularization more frequently than the non-APD(PDR) (P = 0.023). The P ratio was higher in eyes with PDR than in those with NPDR (P < 0.001). The multivariate analysis designated the P ratio (odds ratio, 8.293 × 107; 95% confidence interval, 6.529 × 102-1.053 × 1013; P = 0.002) and the total NPSs (odds ratio, 1.002; 95% confidence interval, 1.001-1.003; P < 0.001) as independent risk factors of PDR. Most eyes with NPDR and 4-2-1 rule findings of DR severity had higher P ratios but not necessarily greater NPS numbers.
UNASSIGNED: The APD(PDR) is uniquely distributed on widefield OCTA images, and the NPA location patterns are associated with DR severity, independent of the entire area of NPAs.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.

UNASSIGNED: To report different biometric measurements in high myopia associated with different underlying ocular and genetic conditions.
UNASSIGNED: Retrospective study.
UNASSIGNED: Patients with high myopia.
UNASSIGNED: We searched the Stanford Research Repository tool to identify patients with the diagnosis of high myopia who were seen by a single provider at Byers Eye Institute at Stanford from January 2019 to March 2022. We performed a chart review and included eyes that had high myopia and ocular biometric measurements at any time point after January 2019. We divided our cohort into 5 different groups: (1) isolated high myopia (IHM) (control group); (2) retinopathy of prematurity (ROP); (3) familial exudative vitreoretinopathy; (4) Marfan syndrome; and (5) Stickler syndrome.
UNASSIGNED: Biometric measurements.
UNASSIGNED: A total of 246 patients (432 eyes) were included as follows: 202 patients (359 eyes) in the IHM group, 17 patients (27 eyes) in the ROP group, 7 patients (12 eyes) in the familial exudative vitreoretinopathy group, 8 patients (14 eyes) in the Marfan group, and 12 patients (20 eyes) in the Stickler group. The ROP group showed significantly shorter axial lengths, shallower anterior chambers, and thicker lenses compared with the IHM group. The Marfan group showed significantly flatter corneas and thicker lenses compared with the IHM group. The Stickler group showed significantly longer axial lengths compared with the IHM group.
UNASSIGNED: High myopia is associated with variable biometric measurements according to underlying ocular or genetic conditions. Retinopathy of prematurity-associated high myopia is primarily lenticular, while Stickler syndrome-associated high myopia is axial. Marfan syndrome-associated high myopia is derived from both axial and lenticular mechanisms.

UNASSIGNED: Clinical OCT angiography (OCTA) of the retinal microvasculature offers a quantitative correlate to systemic disease burden and treatment efficacy in sickle cell disease (SCD). The purpose of this study was to use the higher resolution of adaptive optics scanning light ophthalmoscopy (AOSLO) to elucidate OCTA features of parafoveal microvascular compromise identified in SCD patients.
UNASSIGNED: Case series of 11 SCD patients and 1 unaffected control.
UNASSIGNED: A total of 11 eyes of 11 SCD patients (mean age, 33 years; range, 23-44; 8 female, 3 male) and 1 eye of a 34-year-old unaffected control.
UNASSIGNED: Ten sequential 3 × 3 mm parafoveal OCTA full vascular slab scans were obtained per eye using a commercial spectral domain OCT system (Avanti RTVue-XR; Optovue). These were used to identify areas of compromised perfusion near the foveal avascular zone (FAZ), designated as regions of interest (ROIs). Immediately thereafter, AOSLO imaging was performed on these ROIs to examine the cellular details of abnormal perfusion. Each participant was imaged at a single cross-sectional time point. Additionally, 2 of the SCD patients were imaged prospectively 2 months after initial imaging to study compromised capillary segments across time and with treatment.
UNASSIGNED: Detection and characterization of parafoveal perfusion abnormalities identified using OCTA and resolved using AOSLO imaging.
UNASSIGNED: We found evidence of abnormal blood flow on OCTA and AOSLO imaging among all 11 SCD patients with diverse systemic and ocular histories. Adaptive optics scanning light ophthalmoscopy imaging revealed a spectrum of phenomena, including capillaries with intermittent blood flow, blood cell stasis, and sites of thrombus formation. Adaptive optics scanning light ophthalmoscopy imaging was able to resolve single sickled red blood cells, rouleaux formations, and blood cell-vessel wall interactions. OCT angiography and AOSLO imaging were sensitive enough to document improved retinal perfusion in an SCD patient 2 months after initiation of oral hydroxyurea therapy.
UNASSIGNED: Adaptive optics scanning light ophthalmoscopy imaging was able to reveal the cellular details of perfusion abnormalities detected using clinical OCTA. The synergy between these clinical and laboratory imaging modalities presents a promising avenue in the management of SCD through the development of noninvasive ocular biomarkers to prognosticate progression and measure the response to systemic treatment.

UNASSIGNED: A deep learning model was developed to detect nonexudative macular neovascularization (neMNV) using OCT B-scans.
UNASSIGNED: Retrospective review of a prospective, observational study.
UNASSIGNED: Normal control eyes and patients with age-related macular degeneration (AMD) with and without neMNV.
UNASSIGNED: Swept-source OCT angiography (SS-OCTA) imaging (PLEX Elite 9000, Carl Zeiss Meditec, Inc) was performed using the 6 × 6-mm scan pattern. Individual B-scans were annotated to distinguish between drusen and the double-layer sign (DLS) associated with the neMNV. The machine learning model was tested on a dataset graded by humans, and model performance was compared with the human graders.
UNASSIGNED: Intersection over Union (IoU) score was measured to evaluate segmentation network performance. Area under the receiver operating characteristic curve values, sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV) were measured to assess the performance of the final classification performance. Chance-corrected agreement between the algorithm and the human grader determinations was measured with Cohen\'s kappa.
UNASSIGNED: A total of 251 eyes from 210 patients, including 182 eyes with DLS and 115 eyes with drusen, were used for model training. Of 125 500 B-scans, 6879 B-scans were manually annotated. A vision transformer segmentation model was built to extract DLS and drusen from B-scans. The extracted prediction masks from all B-scans in a volume were projected to an en face image, and an eye-level projection map was obtained for each eye. A binary classification algorithm was established to identify eyes with neMNV from the projection map. The algorithm achieved 82%, 90%, 79%, and 91% sensitivity, specificity, PPV, and NPV, respectively, on a separate test set of 100 eyes that were evaluated by human graders in a previous study. The area under the curve value was calculated as 0.91 (95% confidence interval, 0.85-0.98). The results of the algorithm showed excellent agreement with the senior human grader (kappa = 0.83, P < 0.001) and moderate agreement with the junior grader consensus (kappa = 0.54, P < 0.001).
UNASSIGNED: Our network (code is available at https://github.com/uw-biomedical-ml/double_layer_vit) was able to detect the presence of neMNV from structural B-scans alone by applying a purely transformer-based model.