Chronic central serous chorioretinopathy (cCSC) is characterized by persistent subretinal fluid (SRF), and long-term anatomical response to navigated subthreshold micropulse laser (NSML) varies among patients. This study evaluated 24-month anatomical and functional outcomes after NSML and investigated baseline optical coherence tomography (OCT) parameters associated with treatment response. This retrospective longitudinal study included 61 eyes with treatment-naïve cCSC treated with NSML and followed for 24 months. Eyes were classified as responders or nonresponders according to complete resolution or persistence of subretinal fluid (SRF) at month 24. Baseline and 24-month OCT parameters, including SRF height, subretinal hyperreflective material (SHRM) height and width, choroidal thickness, Haller layer thickness, and Sattler/choriocapillaris complex thickness, were compared within and between groups. Pearson correlation analysis assessed associations between longitudinal OCT changes and best-corrected visual acuity (BCVA). Multivariable logistic regression was performed to identify independent baseline predictors of anatomical response. At 24 months, 33 eyes (54.1
The US Food and Drug Administration (FDA) approved avacincaptad pegol (ACP) 2 mg in August 2023 for the treatment of geographic atrophy (GA). With limited data published on real-world use, this study evaluates patient characteristics, treatment patterns, and adverse event (AE) profiles in routine clinical care of patients with GA who received ACP 2 mg during the first year after FDA approval. This noninterventional cohort study of secondary use data from the American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) followed patients with GA (aged ≥ 50 years at diagnosis) who received their first ACP 2 mg injection (index date) between 1 September 2023 and 31 August 2024, until ACP discontinuation, switching to another GA treatment, loss to follow-up, or end of study. A total of 10,181 patients (13,391 patient-eyes) were included. Mean age was 81.7 years and 65.6
Evidence regarding minimally invasive glaucoma surgery in very old patients remains limited, making surgical decision-making challenging in this population. We investigated age-related differences in the safety and efficacy of microhook ab interno trabeculotomy combined with cataract surgery (combined µLOT). This single-center retrospective study in Japan included 67 consecutive glaucomatous eyes of 67 patients who underwent combined µLOT and were followed for at least 6 months. The cohort comprised 27 male and 40 female patients. Patients were divided by median age into younger (< 76 years, n = 32) and older (≥ 76 years, n = 35) groups. Qualified success was defined irrespective of postoperative glaucoma medication use and without the need for additional glaucoma surgery, and was evaluated using three intraocular pressure (IOP) criteria: criterion A, 6 ≤ IOP ≤ 18 mmHg; criterion B, 6 ≤ IOP ≤ 15 mmHg; and criterion C, 6 ≤ IOP ≤ 13 mmHg. The mean age of the older group was 82.5 years. At 6 months, qualified success probabilities were 94.0
Current AI models for optical coherence tomography (OCT) primarily focus on diagnosis as a standalone task. We developed and validated an end-to-end agentic AI workflow that integrates automated macular hole (MH) diagnosis from OCT images with structured patient-friendly report generation. The proposed sequential workflow coordinated by a workflow orchestration agent consisted of an image diagnosis stage and a language generation step. The OCT version of RETFound, a self-supervised retinal foundation model, was fine-tuned for binary classification of macular OCT scans as MH or normal macula. Training, validation, and internal testing were performed using multiple public OCT datasets with patient-level stratified splitting, followed by external validation on two independent datasets. The performance of the fine-tuned foundation model was evaluated using accuracy, sensitivity, specificity, and receiver operating characteristic area under the curve (ROC AUC). For each new OCT image, the AI agent used the trained RETFound model to classify the image, received the diagnostic output, incorporated it into a structured prompt, and called the large language model (GPT-4) to generate the clinical report. On the internal test set (n = 180), the model achieved an accuracy of 100
