Background: The retina and the brain share anatomic, embryologic, and physiologic characteristics. Therefore, retinal imaging in patients with brain disorders has been of significant interest. Using optical coherence tomography angiography (OCTA), a novel quantitative method of measuring retinal vasculature, we aimed to evaluate radial peripapillary capillary (RPC) network density and retinal nerve fiber layer (RNFL) thickness in cognitively impaired patients and determine their association with brain imaging markers. Methods: In this prospective cross-sectional study, a total of 69 patients (138 eyes) including 29 patients with amyloid-positive Alzheimer’s disease-related cognitive impairment (ADCI), 25 patients with subcortical vascular cognitive impairment (SVCI), and 15 amyloid-negative cognitively normal (CN) subjects were enrolled. After excluding eyes with an ophthalmologic disease or poor image quality, 117 eyes of 60 subjects were included in the final analyses. Retinal vascular [capillary density (CD) of the radial peripapillary capillary (RPC) network] and neurodegeneration markers [retinal nerve fiber layer (RNFL) thickness at four quadrants] were measured using OCTA and OCT imaging. Brain vascular (CSVD score) and neurodegeneration markers (cortical thickness) were assessed using 3D brain magnetic resonance imaging. The CD and RNFL thickness and their correlation with brain imaging markers were investigated. Results: The SVCI group showed lower CD in the temporal quadrant of the RPC network compared to the CN group (mean (SD), 42.34 (6.29) vs 48.45 (7.08); p = 0.001). When compared to the ADCI group, the SVCI showed lower CD in the superior quadrant (mean (SD), 60.14 (6.42) vs 64.15 (6.39); p = 0. 033) as well as in the temporal quadrant (ADCI 45.76, SVCI 42.34; p = 0.048) of the RPC network. The CD was negatively correlated with CSVD score in the superior (B (95%CI), − 0.059 (− 0.097 to − 0.021); p = 0.003) and temporal (B (95%CI), − 0.048 (− 0.080 to − 0.017); p = 0.003) quadrants of the RPC network. RNFL thickness did not differ among the groups nor did it correlate with cortical thickness. Conclusions and relevance: The microvasculature of the RPC network was related to the CSVD burden. However, the RNFL thickness did not reflect cerebral neurodegeneration. Noninvasive and rapid acquisition of the OCTA image might have the potential to be used as a screening tool to detect CSVD.

Background: Accurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a large number of patients for amblyopia risk. Objective: For efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep learning–based system to predict the range of refractive error in children (mean age 4.32 years, SD 1.87 years) using 305 eccentric photorefraction images captured with a smartphone. Methods: Photorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction. Results: The trained deep learning model had an overall accuracy of 81.6%, with the following accuracies for each refractive error class: 80.0% for ≤−5.0 diopters (D), 77.8% for >−5.0 D and ≤−3.0 D, 82.0% for >−3.0 D and ≤−0.5 D, 83.3% for >−0.5 D and <+0.5 D, 82.8% for ≥+0.5 D and <+3.0 D, 79.3% for ≥+3.0 D and <+5.0 D, and 75.0% for ≥+5.0 D. These results indicate that our deep learning–based system performed sufficiently accurately. Conclusions: This study demonstrated the potential of precise smartphone-based prediction systems for refractive error using deep learning and further yielded a robust collection of pediatric photorefraction images.

We investigated the characteristics of children with peripapillary hyperreflective ovoid mass-like structures (PHOMS) and evaluated the associated risk factors. This cross-sectional study included 132 eyes of 66 children with PHOMS and 92 eyes of 46 children without PHOMS (controls) who were assessed by disc enhanced-depth image spectral-domain (SD) optical coherence tomography (OCT). Univariable and multivariable logistic analyses were performed to evaluate risk factors associated with presence of PHOMS. Among the 66 children with PHOMS, 53 (80.3%) had bilateral and 13 (19.7%) had unilateral PHOMS. The mean age of the PHOMS group was 11.7 ± 2.6 years, and that of the control group was 11.4 ± 3.1 years. The mean spherical equivalent (SE) as determined by cycloplegic refraction was −3.13 ± 1.87 diopters (D) in the PHOMS group and −0.95 ± 2.65 D in the control group. Additionally, mean astigmatism was 0.67 ± 0.89 D and 0.88 ± 1.02 D in the PHOMS group and the control group, respectively. Mean disc size was 1,735 ± 153 µm in the PHOMS group and 1,741 ± 190 µm in the control group, while mean optic nerve head (ONH) tilt angle was 9.84 ± 5.38 degrees in the PHOMS group and 3.71 ± 4.41 degrees in the control group. SE and ONH tilt angle were significantly associated with PHOMS according to both univariable [odds ratio (OR): 1.59; p < 0.001 and OR: 1.35; p < 0.001, respectively] and multivariable (OR: 1.71; p = 0.001 and OR: 1.29; p = 0.001, respectively) logistic regression analyses. There was a significant correlation between SE and ONH tilt (r = −0.46; p < 0.001). In conclusion, PHOMS is associated with myopic shift in children, and optic disc tilt may be a mediator between myopia and PHOMS.

We have quantitatively evaluated the macular and peripapillary microvascular changes in eyes with chiasmal compression caused by brain tumors compared with healthy control eyes using optical coherence tomography angiography (OCT-A) and correlated them with other ocular parameters. This cross-sectional study involved the analysis of 36 eyes of 36 patients with chiasmal compression and age and refractive error-matched 35 healthy control eyes. OCT-A was used to generate microvascular images of the superficial and deep retinal capillary plexus (SRCP, DRCP) and the radial peripapillary capillary (RPC) segment in the macula and peripapillary areas. Automated segmentation and vessel density measurements facilitated the analysis of each layer. Macular OCT-A analysis revealed a significant reduction in vessel density in the SRCP (P = 0.004) of the nasal quadrant (P < 0.001) and in the same quadrant of the DRCP (P = 0.019) in the eyes with chiasmal compression compared with the control eyes. The RPC segment vessel density has also been significantly reduced in the eyes with chiasmal compression (P < 0.001). The RPC segment and the SRCP vessel densities were correlated with the peripapillary retinal nerve fiber layer and the ganglion cell layer complex thicknesses. The RPC segment and the nasal quadrant SRCP and the DRCP vessel densities were correlated with visual field defect. Significant microvascular alterations have been detected in the eyes with chiasmal compression compared with the control eyes. This study confirmed that chiasmal compression caused by brain tumors not only induced a loss of ganglion cells but also resulted in intra-retinal microvascular changes.