Primary open-angle glaucoma (POAG) is often caused by elevated intraocular pressure (IOP), which arises due to increased resistance to aqueous humor outflow (AHO). Aqueous humor flows through Schlemm’s canal (SC), a lymphatic-like vessel encircling the cornea, and via intercellular spaces of ciliary muscle cells. However, the mechanisms underlying increased AHO resistance are poorly understood. Here, we demonstrate that signaling between angiopoietin (Angpt) and the Angpt receptor Tie2, which is critical for SC formation, is also indispensable for maintaining SC integrity during adulthood. Deletion of Angpt1/Angpt2 or Tie2 in adult mice severely impaired SC integrity and transcytosis, leading to elevated IOP, retinal neuron damage, and impairment of retinal ganglion cell function, all hallmarks of POAG in humans. We found that SC integrity is maintained by interconnected and coordinated functions of Angpt-Tie2 signaling, AHO, and Prox1 activity. These functions diminish in the SC during aging, leading to impaired integrity and transcytosis. Intriguingly, Tie2 reactivation using a Tie2 agonistic antibody rescued the POAG phenotype in Angpt1/Angpt2-deficient mice and rejuvenated the SC in aged mice. These results indicate that the Angpt-Tie2 system is essential for SC integrity. The impairment of this system underlies POAG-associated pathogenesis, supporting the possibility that Tie2 agonists could be a therapeutic option for glaucoma.

Cardiovascular disease (CVD), the leading cause of death globally, is associated with complicated underlying risk factors. We develop an artificial intelligence model to identify CVD using multimodal data, including clinical risk factors and fundus photographs from the Samsung Medical Center (SMC) for development and internal validation and from the UK Biobank for external validation. The multimodal model achieves an area under the receiver operating characteristic curve (AUROC) of 0.781 (95% confidence interval [CI] 0.766–0.798) in the SMC and 0.872 (95% CI 0.857–0.886) in the UK Biobank. We further observe a significant association between the incidence of CVD and the predicted risk from at-risk patients in the UK Biobank (hazard ratio [HR] 6.28, 95% CI 4.72–8.34). We visualize the importance of individual features in photography and traditional risk factors. The results highlight that non-invasive fundus photography can be a possible predictive marker for CVD.

In recent years, various types of presbyopia-correcting intraocular lenses (IOLs) have been developed to achieve clear intermediate and near visual acuity without glasses following cataract surgery. These include conventional multifocal (bifocal, trifocal, quadrifocal), accommodative, and extended depth-of-focus (EDOF) IOLs.^1-5 In contrast to the standard monofocal IOLs that provide patients with good visual acuity at 1 focal point,¹ multifocal IOLs can produce more than 1 focus based on refractive or diffractive optics, thereby affording adequate far and near vision.⁴ Thus, they minimize spectacle dependency after cataract surgery. However, patients may experience difficulties, such as reduced contrast sensitivity, glare, halos, or blurred vision, which may stem from the structural principles of multifocal IOLs.²

Recent studies suggested that an association exists between vision loss and cognitive impairment, although it is still vague whether there are causal relationships or direct association between low vision and dementia. We were to investigate the association between low vision and dementia in the Korean population using the National Health Insurance Service (NHIS) database. We analyzed the data of 6,029,657 subjects aged ≥40 years, drawn from Korea National Health Insurance Service. The hazard ratio (HRs) and 95% confidence interval (CIs) of dementia, Alzheimer’s disease (AD), and Vascular dementia (VD) were estimated using multivariable Cox proportional hazards regression models. Statistical analysis showed that subjects with more severe visual impairments have a higher risk of dementia, AD, and VD after adjusting for compounding variables. The HRs of dementia increased significantly as visual acuity worsened (HRs 1.444 [95% CIs 1.415–1.473] for visual acuity (VA) < 1.0, 1.734 [1.693–1.777] for VA < 0.3, 1.727 [1.686–1.770] for VA < 0.1 and 1.991[1.902–2.085] for visual loss). Baseline visual loss and visual impairment were positively associated with the risk of dementia, AD, and VD. From the results of this nationwide population-based cohort study, we suggest that there is a significant increase in the incidence of dementia in subjects with low vision.

To develop and validate an automated deep learning (DL)-based artificial intelligence (AI) platform for diagnosing and grading cataracts using slit-lamp and retroillumination lens photographs based on the Lens Opacities Classification System (LOCS) III. Cross-sectional study in which a convolutional neural network was trained and tested using photographs of slit-lamp and retroillumination lens photographs. One thousand three hundred thirty-five slit-lamp images and 637 retroillumination lens images from 596 patients. Slit-lamp and retroillumination lens photographs were graded by 2 trained graders using LOCS III. Image datasets were labeled and divided into training, validation, and test datasets. We trained and validated AI platforms with 4 key strategies in the AI domain: (1) region detection network for redundant information inside data, (2) data augmentation and transfer learning for the small dataset size problem, (3) generalized cross-entropy loss for dataset bias, and (4) class balanced loss for class imbalance problems. The performance of the AI platform was reinforced with an ensemble of 3 AI algorithms: ResNet18, WideResNet50-2, and ResNext50. Diagnostic and LOCS III-based grading prediction performance of AI platforms. The AI platform showed robust diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.9992 [95% confidence interval (CI), 0.9986–0.9998] and 0.9994 [95% CI, 0.9989–0.9998]; accuracy, 98.82% [95% CI, 97.7%–99.9%] and 98.51% [95% CI, 97.4%–99.6%]) and LOCS III-based grading prediction performance (AUC, 0.9567 [95% CI, 0.9501–0.9633] and 0.9650 [95% CI, 0.9509–0.9792]; accuracy, 91.22% [95% CI, 89.4%–93.0%] and 90.26% [95% CI, 88.6%–91.9%]) for nuclear opalescence (NO) and nuclear color (NC) using slit-lamp photographs, respectively. For cortical opacity (CO) and posterior subcapsular opacity (PSC), the system achieved high diagnostic performance (AUC, 0.9680 [95% CI, 0.9579–0.9781] and 0.9465 [95% CI, 0.9348–0.9582]; accuracy, 96.21% [95% CI, 94.4%–98.0%] and 92.17% [95% CI, 88.6%–95.8%]) and good LOCS III-based grading prediction performance (AUC, 0.9044 [95% CI, 0.8958–0.9129] and 0.9174 [95% CI, 0.9055–0.9295]; accuracy, 91.33% [95% CI, 89.7%–93.0%] and 87.89% [95% CI, 85.6%–90.2%]) using retroillumination images. Our DL-based AI platform successfully yielded accurate and precise detection and grading of NO and NC in 7-level classification and CO and PSC in 6-level classification, overcoming the limitations of medical databases such as few training data or biased label distribution.