Minimally invasive glaucoma surgery (MIGS) has emerged as a novel approach in the glaucoma treatment spectrum, offering a range of diverse procedures and devices aimed at reducing intraocular pressure (IOP). MIGS can be broadly classified into several categories: those that enhance trabecular outflow (Trabectome, iStent, Hydrus Microstent, Kahook Dual Blade, high frequency deep sclerotomy, and gonioscopy-assisted transluminal trabeculotomy), those that augment suprachoroidal outflow (CyPass Microstent and iStent Supra), those that target Schlemm’s canal (TRAB360 and the OMNI Surgical System, Streamline, and Ab Interno Canaloplasty), and conjunctival bleb-forming procedures (EX-PRESS Glaucoma Filtration Device, Xen Gel Stent and PreserFlo MicroShunt). MIGS is considered to have a shorter surgical time and fewer severe complications when compared to traditional glaucoma surgeries such as trabeculectomy and glaucoma drainage device implantation (Ahmed, Baerveldt, and Molteno valves). This literature review comprehensively examines the distinct MIGS devices and procedures, their underlying mechanisms, and clinical outcomes, emphasizing the importance of evaluating the efficacy and complications of each approach individually. As the field of MIGS continues to evolve, it is crucial to prioritize high-quality, long-term studies to better understand the safety and effectiveness of these innovative interventions in glaucoma management.

Aims To develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts. Methods We trained a deep convolutional ensemble (DCE), an ensemble of five convolutional neural networks (CNNs), to classify retinal fundus photographs into diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and normal eyes. The CNN architecture was based on the InceptionV3 model, and initial weights were pretrained on the ImageNet dataset. We used 43 055 fundus images from 12 public datasets. Five trained ensembles were then tested on an ‘unseen’ set of 100 images. Seven board-certified ophthalmologists were asked to classify these test images. Results Board-certified ophthalmologists achieved a mean accuracy of 72.7% over all classes, while the DCE achieved a mean accuracy of 79.2% (p=0.03). The DCE had a statistically significant higher mean F1-score for DR classification compared with the ophthalmologists (76.8% vs 57.5%; p=0.01) and greater but statistically non-significant mean F1-scores for glaucoma (83.9% vs 75.7%; p=0.10), AMD (85.9% vs 85.2%; p=0.69) and normal eyes (73.0% vs 70.5%; p=0.39). The DCE had a greater mean agreement between accuracy and confident of 81.6% vs 70.3% (p<0.001). Discussion We developed a deep learning model and found that it could more accurately and reliably classify four categories of fundus images compared with board-certified ophthalmologists. This work provides proof-of-principle that an algorithm is capable of accurate and reliable recognition of multiple retinal diseases using only fundus photographs.

This report describes the case of a 27-year-old lady who presented with sudden decrease in vision in the right eye for one week. She was suffering from constipation and had a history of straining while passing stools. She was found to have two large sub-internal limiting membrane haemorrhages with the classical double ring sign, which were drained using laser membranotomy, following which her vision rapidly improved. This case of two sub-internal limiting membrane haemorrhages is reported for its rarity and also highlights the usefulness of laser hyaloidotomy or membranotomy in large haemorrhages of recent onset. The literature was also reviewed to discuss the other treatment options in brief.

Glaucoma, the second leading cause of irreversible blindness globally, encompasses a heterogeneous group of ocular disorders characterized by the progressive degeneration of retinal ganglion cells. Pharmacotherapy remains the cornerstone of treatment, primarily aimed at reducing intraocular pressure (IOP) by decreasing aqueous humor production or enhancing its outflow. The therapeutic classes employed include carbonic anhydrase inhibitors, β-blockers, α-adrenergic agonists, prostaglandin analogs, parasympathomimetics, Rho kinase inhibitors, and hyperosmotic agents. Despite their efficacy, these medications are associated with a range of ocular and systemic side effects, influenced by their mechanisms of action, formulation, and dosage. Ocular adverse effects, such as irritation, dry eye, allergic reactions, and infections, are common, while systemic absorption may lead to more severe outcomes, including organ dysfunction, exacerbation of comorbid conditions, or life-threatening cardiovascular events. Given these potential risks, it is critical for clinicians to understand and monitor these adverse effects as they significantly affect patient adherence, quality of life, and treatment outcomes. Ongoing research is essential to develop novel therapeutic regimens, agents, or delivery methods that minimize side effects and improve compliance. Incorporating patient-reported outcomes in clinical practice may further enhance the assessment of treatment impact, facilitating more tailored and effective management of glaucoma.

Glaucoma refers to a spectrum of progressive optic neuropathies and remains the leading cause of irreversible blindness worldwide. Its insidious onset poses serious challenges to conventional diagnostic methods and clinicians striving to detect early-stage disease for timely and effective intervention. Artificial intelligence (AI) has demonstrated its ability to process and analyze large datasets which can help identify subtle changes in early glaucomatous clinical presentation. This study reviews the current state of AI utilization in glaucoma and elucidates the strengths and limitations of existing approaches. We dissect the role of AI in various domains: enhancing early detection and diagnosis, monitoring disease progression, and refining treatment strategies to optimize patient outcomes. Furthermore, we address the ethical, legal, and social implications, alongside the inherent limitations of AI in the clinical setting. Despite these challenges, AI holds transformative potential for glaucoma management. Future directions emphasize the need for interdisciplinary collaboration, advanced and explainable algorithm development, and equitable healthcare access to fully realize the promise of AI in combating this vision-threatening condition.

Purpose: This study evaluated the effect of preoperative brimonidine tartrate 0.2% on intraocular pressure (IOP) during robotic-assisted laparoscopic radical prostatectomy in steep Trendelenburg position (sTBURG). Materials and Methods: In this prospective randomized controlled masked interventional trial, eligible patients scheduled for robotic-assisted laparoscopic radical prostatectomy in sTBURG at the Toronto General Hospital had one eye randomized to placebo (artificial tears) or drug (brimonidine tartrate 0.2%) preoperatively. Visual acuity (VA), tonometry, disc photography, visual field (VF), and retinal nerve fiber layer (RNFL) assessments were performed preoperatively and postoperatively. A standardized anesthetic protocol was followed intraoperatively. IOP was measured using Tono-Pen AVIA (Reichert Inc., New York, NY) as follows: preanesthesia supine, anesthetized supine, hourly in sTBURG and awake supine. The primary outcome was IOP in sTBURG in the drug group compared with the placebo group. Secondary outcomes were changes in VA, VF, RNFL thickness, mean arterial pressure, and ocular perfusion pressure. This study was approved by University Health Network Research Ethics Board. Results: In total, 26 eligible patients, mean age 61.9±5.1 years, were randomized to brimonidine (11 patients) and placebo (15 patients). Baseline IOP was not significantly different between the drug and placebo groups (P=0.42). Significant and sustained IOP elevation of >1.5X baseline in the sTBURG was noted in both groups. The mean IOP 1 hour after sTBURG was 29.4±6.9 and 27.2±3.4 mm Hg in the drug and placebo groups, respectively (P=0.35). No significant changes were noted in VA, VF, or RNFL. Conclusions: Significant and sustained IOP increases occur during sTBURG. Preoperative brimonidine does not prevent IOP spikes in sTBURG.