The fovea undergoes significant developmental changes from birth into adolescence. However, there is limited data examining cone photoreceptor density, foveal pit shape, and foveal avascular zone (FAZ) size in children. The purpose of this study was to determine whether overall foveal structure differs as a function of age and refractive status in children. Forty-eight healthy children (ages 5.8 to 15.8 years) underwent optical coherence tomography imaging to quantify foveal point thickness and foveal pit diameter, depth, and slope. Adaptive optics scanning laser ophthalmoscope (AOSLO) images of foveal capillaries and cone photoreceptors were acquired in a subset of children to quantify FAZ metrics and cone densities at 0.2, 0.3, and 0.5 mm eccentricities. Results show that foveal pit and FAZ metrics were not related to age, axial length, or refractive status. However, linear cone density was lower in myopic versus non-myopic children at eccentricities of 0.2 mm (mean ± SD = 50,022 ± 5,878 cones/mm2 vs 58,989 ± 4,822 cones/mm2, P < 0.001) and 0.3 mm (43,944 ± 5,547 cones/mm2 vs 48,622 ± 3,538 cones/mm2, P < 0.001). These results suggest FAZ and foveal pit metrics do not systematically differ with age in children, while myopic eyes have decreased linear cone density near the foveal center. Significance Statement: The development of the fovea begins prior to birth and continues through the early teenage years until it reaches adult-like properties. Although the majority of changes during childhood are related to the maturation and migration of cone photoreceptors, in vivo data describing cone packing in children is limited. We assessed overall foveal structure in children as young as 5.8 years old by quantifying cone density and spacing, foveal avascular zone size, and foveal pit morphometry to investigate potential structural differences as a function of age and refractive status. While foveal avascular zone and foveal pit metrics did not significantly differ with age, results indicate that myopic children have lower linear cone densities close to the foveal center compared to non-myopic children.

Purpose Segmentation and evaluation of in vivo confocal microscopy (IVCM) images requires manual intervention, which is time consuming, laborious, and non-reproducible. The aim of this research was to develop and validate deep learning–based methods that could automatically segment and evaluate corneal nerve fibers (CNFs) and dendritic cells (DCs) in IVCM images, thereby reducing processing time to analyze larger volumes of clinical images. Methods CNF and DC segmentation models were developed based on U-Net and Mask R-CNN architectures, respectively; 10-fold cross-validation was used to evaluate both models. The CNF model was trained and tested using 1097 and 122 images, and the DC model was trained and tested using 679 and 75 images, respectively, at each fold. The CNF morphology, number of nerves, number of branching points, nerve length, and tortuosity were analyzed; for DCs, number, size, and immature–mature cells were analyzed. Python-based software was written for model training, testing, and automatic morphometric parameters evaluation. Results The CNF model achieved on average 86.1% sensitivity and 90.1% specificity, and the DC model achieved on average 89.37% precision, 94.43% recall, and 91.83% F 1 score. The interclass correlation coefficient (ICC) between manual annotation and automatic segmentation were 0.85, 0.87, 0.95, and 0.88 for CNF number, length, branching points, and tortuosity, respectively, and the ICC for DC number and size were 0.95 and 0.92, respectively. Conclusions Our proposed methods demonstrated reliable consistency between manual annotation and automatic segmentation of CNF and DC with rapid speed. The results showed that these approaches have the potential to be implemented into clinical practice in IVCM images. Translational Relevance The deep learning–based automatic segmentation and quantification algorithm significantly increases the efficiency of evaluating IVCM images, thereby supporting and potentially improving the diagnosis and treatment of ocular surface disease associated with corneal nerves and dendritic cells.

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