Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

Background: The rapid advancement in artificial intelligence (AI) has fundamentally reshaped gut microbiome research by enabling high-resolution analysis of complex, high-dimensional microbial communities and their functional interactions with the human host. Objective: This narrative review aims to synthesize recent methodological advances in AI-driven gut microbiome research and to evaluate their translational relevance for therapeutic optimization, personalized nutrition, and precision medicine. Methods: A narrative literature review was conducted using PubMed, Google Scholar, Web of Science, and IEEE Xplore, focusing on peer-reviewed studies published between approximately 2015 and early 2025. Representative articles were selected based on relevance to AI methodologies applied to gut microbiome analysis, including machine learning, deep learning, transformer-based models, graph neural networks, generative AI, and multi-omics integration frameworks. Additional seminal studies were identified through manual screening of reference lists. Results: The reviewed literature demonstrates that AI enables robust identification of diagnostic microbial signatures, prediction of individual responses to microbiome-targeted therapies, and design of personalized nutritional and pharmacological interventions using in silico simulations and digital twin models. AI-driven multi-omics integration—encompassing metagenomics, metatranscriptomics, metabolomics, proteomics, and clinical data—has improved functional interpretation of host–microbiome interactions and enhanced predictive performance across diverse disease contexts. For example, AI-guided personalized nutrition models have achieved AUC exceeding 0.8 for predicting postprandial glycemic responses, while community-scale metabolic modeling frameworks have accurately forecast individualized short-chain fatty acid production. Conclusions: Despite substantial progress, key challenges remain, including data heterogeneity, limited model interpretability, population bias, and barriers to clinical deployment. Future research should prioritize standardized data pipelines, explainable and privacy-preserving AI frameworks, and broader population representation. Collectively, these advances position AI as a cornerstone technology for translating gut microbiome data into actionable insights for diagnostics, therapeutics, and precision nutrition.

Background/Aims: Primary open-angle glaucoma (POAG) disproportionately affects individuals of African ancestry. In these patients’ eyes, a large cup-to-disc ratio (LCDR > 0.90) suggests greater retinal ganglion cell loss, though these patients often display varied visual ability. This study investigated the prevalence and risk factors associated with LCDR in African ancestry individuals with POAG and explored the differences between blind (>20/200) and not blind (≤20/200) LCDR eyes. Methods: A case–control methodology was used to investigate the demographic, optic disc, and genetic risk factors of subjects in the Primary Open-Angle African American Glaucoma Genetics Study. Risk factors were analyzed using univariable and multivariable logistic regression models with inter-eye correlation adjusted using generalized estimating equations. Results: Out of 5605 eyes with POAG, 1440 eyes (25.7%) had LCDR. In the multivariable analysis, LCDR was associated with previous glaucoma surgery (OR = 1.72), increased intraocular pressure (OR = 1.04), decreased mean deviation (OR = 1.08), increased pattern standard deviation (OR = 1.06), thinner retinal nerve fiber layer (OR = 1.05), nasalization of vessels (OR = 2.67), bayonetting of vessels (OR = 1.98), visible pores in the lamina cribrosa (OR = 1.68), and a bean-shaped cup (OR = 2.11). Of LCDR eyes, 30.1% were classified as blind (≤20/200). In the multivariable analysis, the statistically significant risk factors of blindness in LCDR eyes were previous glaucoma surgery (OR = 1.72), increased intraocular pressure (OR = 1.05), decreased mean deviation (OR = 1.04), and decreased pattern standard deviation (OR = 0.90). Conclusions: These findings underscore the importance of close monitoring of intraocular pressure and visual function in African ancestry POAG patients, particularly those with LCDR, to preserve visual function.

Glaucoma is a group of progressive optic neuropathies characterized by the degeneration of retinal ganglion cells (RGCs) and their axons, leading to irreversible vision loss or blindness if left untreated. Current glaucoma treatments primarily focus on lowering intraocular pressure (IOP), the only proven method to slow disease progression. However, these treatments do not effectively address RGC loss or promote optic nerve regeneration. Emerging research into both natural and synthetic bioactive compounds offers promising new avenues for glaucoma management. This review explores the multifaceted therapeutic potential of bioactive compounds in glaucoma treatment, focusing on their mechanisms of action in IOP reduction and neuroprotection, as well as antioxidant and anti-inflammatory effects. We further review ongoing clinical trials and discuss significant challenges to clinical translation, including the issues of bioavailability, standardization, long-term safety, and regulatory challenges. Furthermore, this paper highlights the potential of integrating precision medicine into bioactive treatments, emphasizing the value of personalized care based on genetic and phenotypic profiles. Finally, the review discusses the role of combination therapies, which leverage the complementary mechanisms of bioactive compounds and conventional treatments. As research progresses, the development of targeted, bioactive-based therapies could transform glaucoma management, offering more comprehensive and effective options for patients with this vision-threatening disease.

To identify risk factors associated with conversion from glaucoma suspect to primary open-angle glaucoma (POAG) in individuals of African ancestry. Prospective case-control study. A total of 330 glaucoma suspects from the Primary Open-Angle African American Glaucoma Genetics study with a median follow-up of 6.71 (IQR: 3.73-9.72) years. Participants were recruited from ophthalmology clinics in Philadelphia between July 2010 and August 2024. Participants were classified as glaucoma suspects using quantitative adaptations of the American Academy of Ophthalmology definition if they met at least one of the following criteria: (1) elevated intraocular pressure (IOP), (2) suspicious optic nerve head or retinal nerve fiber layer (RNFL) appearance, or (3) suspicious visual field results. POAG was defined as the development of characteristic glaucomatous optic nerve damage with corresponding visual field defects. Demographic, clinical, and ocular parameters were measured at baseline and during follow-up examinations. Conversion from glaucoma suspect to POAG. Risk factors were assessed using univariable and multivariable Cox proportional hazard models. During the follow-up period, 45 (13.6%) out of 330 glaucoma suspects converted to POAG cases. In multivariate analysis, 3 factors were significantly associated with increased risk of POAG conversion: elevated IOP (≥21 mmHg) (HR: 2.49, 95% CI: 1.20-5.17; P = .01), absence of hypertension (HR: 2.40, 95% CI: 1.18-4.85; P = .02), and presence of myopia (≤-0.5D) (HR: 2.35, 95% CI: 1.08-5.14; P = .03). Univariate analysis revealed associations between RNFL characteristics and conversion risk, including greater inter-eye difference in RNFL thickness. In glaucoma suspects of African ancestry, higher risk of POAG conversion is independently associated with higher IOP, absence of hypertension, and presence of myopia. These findings can inform targeted screening and management strategies for this high-risk population. The unexpected association between absence of hypertension and increased conversion risk warrants further investigation into the complex relationship between systemic blood pressure and glaucoma pathogenesis in individuals of African ancestry.

There are scarce data regarding the rate of the occurrence of primary open-angle glaucoma (POAG) and visible lamina cribrosa pores (LCPs) in the eyes of individuals with African ancestry; the potential impact of these features on disease burden remains unknown. We recruited subjects with POAG to the Primary Open-Angle African American Glaucoma Genetics (POAAGG) study. Through regression models, we evaluated the association between the presence of LCPs and various phenotypic features. In a multivariable analysis of 1187 glaucomatous eyes, LCPs were found to be more likely to be present in eyes with cup-to-disc ratios (CDR) of ≥0.9 (adjusted risk ratio (aRR) 1.11, 95%CI: 1.04–1.19, p = 0.005), eyes with cylindrical-shaped (aRR 1.22, 95%CI: 1.11–1.33) and bean pot (aRR 1.24, 95%CI: 1.13–1.36) cups versus conical cups (p < 0.0001), moderate cup depth (aRR 1.24, 95%CI: 1.06–1.46) and deep cups (aRR 1.27, 95%CI: 1.07–1.50) compared to shallow cups (p = 0.01), and the nasalization of central retinal vessels (aRR 1.33, 95%CI: 1.23–1.44), p < 0.0001). Eyes with LCPs were more likely to have a higher degree of African ancestry (q0), determined by means of SNP analysis (aRR 0.96, 95%CI: 0.93–0.99, p = 0.005 for per 0.1 increase in q0). Our large cohort of POAG cases of people with African ancestry showed that LCPs may be an important risk factor in identifying severe disease, potentially warranting closer monitoring by physicians.