高山 卓也

Abstract Purpose To investigate the natural course of geographic atrophy (GA) in Japanese patients, with a focus on factors associated with the change of GA size over time and visual acuity decline. Study design Retrospective, single-center observational study. Methods We reviewed 40 eyes of 40 patients with GA and longitudinal fundus autofluorescence (FAF) imaging. Annual progression rates were calculated as square root–transformed (mm/year). Assessed factors included age, sex, smoking history, diabetes, hypertension, baseline best-corrected visual acuity (BCVA), baseline lesion size, foveal center involvement, choroidal thickness, FAF subtypes, and presence of subretinal drusenoid deposits (SDD). Visual outcomes were assessed as change in BCVA (ΔBCVA, logarithm of the minimum angle of resolution (logMAR)). Results Median age was 75.5 years; 50% of eyes had SDD. The median annual GA progression rate was 0.16 mm/year in square root units. Baseline GA area and the presence of SDD were independent predictors of GA progression (β = −0.0837, p = 0.015; β = +0.3064, p = 0.009, respectively). In multivariable analysis, baseline GA area also remained a significant predictor of visual deterioration (β = +0.1369, p = 0.002). When stratified by FAF patterns, there was significant worsening of BCVA in the Diffuse (p = 0.0027) and Banded (p = 0.0117) subtypes, whereas the None and Other subtypes did not show significant changes in BCVA over one year. Conclusions SDD was identified as a significant risk factor for GA progression in Japanese patients. These findings improve understanding of natural history of GA in Japanese population and may inform risk stratification.

UNLABELLED: This study investigated the one-year real-world clinical outcomes of intravitreal faricimab (IVF) in treatment-naïve neovascular age-related macular degeneration (nAMD) and its association with aqueous humor cytokine profiles. Thirty-four eyes of 32 nAMD patients, who received initial IVF at Jichi Medical University were included. These eyes showed significant improvements in central retinal subfield thickness (CST), central choroidal thickness (CCT), and best-corrected visual acuity (BCVA) one year post-IVF (all P < 0.01). Among 24 measurable cytokines, only VEGF-A was significantly higher in eyes with a dry macula at week 16 (P = 0.0030). Three cytokines were significantly higher in eyes with longer injection interval. Higher levels of galectin-1 and VCAM-1, and lower levels of P-selectin, were correlated with greater CST reduction (P = 0.020, 0.0061, and 0.012, respectively). Conversely, five cytokines including Ang-1 were correlated with greater BCVA improvement (all P < 0.05). In conclusion, an anatomical perspective faricimab demonstrated anatomical efficacy for VEGF-driven nAMD in the loading phase, while multiple cytokines associated with vascular instability or fibrosis appear to contribute to its durability. Notably, Ang-1 may be linked to visual improvement, suggesting that the neurotrophic effects of Ang-1 could be enhanced by Ang-2 inhibition. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-025-28911-9.

Retinal breaks are critical lesions that can cause retinal detachment and vision loss if not detected and treated early. Automated, accurate delineation of retinal breaks in ultra-widefield fundus (UWF) images remains challenging. In this study, we developed and validated a deep learning segmentation model based on the PraNet architecture to localize retinal breaks in break-positive cases. We trained and evaluated the model using a dataset comprising 34,867 UWF images of 8083 cases. Performance was assessed using image-level segmentation metrics, including accuracy, precision, recall, Intersection over Union (IoU), dice score, and centroid distance score. The model achieved an accuracy of 0.996, precision of 0.635, recall of 0.756, IoU of 0.539, dice score of 0.652, and centroid distance score of 0.081. To our knowledge, this is the first study to present pixel-level segmentation of retinal breaks in UWF images using deep learning. The proposed PraNet-based model showed high accuracy and robust segmentation performance, highlighting its potential for clinical application.