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Objective: To evaluate efficacy and safety outcomes of the Xen 45 gel stent implant over 24 months of follow-up. Methods: A retrospective analysis of prospectively collected data from the Fight Glaucoma Blindness observational registry. Complete success (CS) was defined as intraocular pressure (IOP) reduction ≥20% from preoperative and an IOP ≤18 mm Hg and ≥6 mm Hg with no secondary procedure at 2 years and without IOP-lowering medications. Qualified success (QS) was defined similarly, allowing the use of IOP-lowering medications. Results: The Xen 45 gel stent implant was implanted in 646 eyes of 515 patients. Preoperative IOP was 21.4±7.6 (mean±SD) mm Hg on 2.7±1.3 IOP-lowering medication and mean deviation was -10.2±8.4 dB. After 24-month follow-up, IOP was 16.8±7.3 mm Hg (mean reduction of 21.7%) on 1.2±1.4 IOP-lowering medications. CS and QS rates at 24 months were 26% and 48%, respectively. CS and QS were higher in the Xen stand-alone group (33% and 52%, respectively) than in the Xen+cataract group (16% and 42%, respectively). Bleb needling was performed in 28.4% of cases, and 18% underwent a secondary procedure. Conclusions: The Xen 45 gel stent implant offers acceptable long-term efficacy for the treatment of open-angle glaucoma. However, there is a significant rate of reoperation and needling, and outcomes are less effective if combined with cataract surgery. [ABSTRACT FROM AUTHOR]

Background/Objectives: Glaucoma is the leading cause of irreversible blindness, with a significant proportion of cases remaining undiagnosed globally. The interpretation of optic disc and retinal nerve fibre layer images poses challenges for optometrists and ophthalmologists, often leading to misdiagnosis. AI has the potential to improve diagnosis. This study aims to validate an AI system (a convolutional neural network based on the Inception-v3 architecture) for detecting glaucomatous optic neuropathy (GON) using colour fundus photographs from a UK population and to compare its performance against Australian optometrists. Methods: A retrospective external validation study was conducted, comparing AI's performance with that of 11 AHPRA-registered optometrists in Australia on colour retinal photographs, evaluated against a reference (gold) standard established by a panel of glaucoma specialists. Statistical analyses were performed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results: For referable GON, the sensitivity of the AI (33.3% [95%CI: 32.4–34.3) was significantly lower than that of optometrists (65.1% [95%CI: 64.1–66.0]), p < 0.0001, although with significantly higher specificity (AI: 97.4% [95%CI: 97.0–97.7]; optometrists: 85.5% [95%CI: 84.8–86.2], p < 0.0001). The optometrists demonstrated significantly higher AUROC (0.753 [95%CI: 0.744–0.762]) compared to AI (0.654 [95%CI: 0.645–0.662], p < 0.0001). Conclusion: The AI system exhibited lower performance than optometrists in detecting referable glaucoma. Our findings suggest that while AI can serve as a screening tool, both AI and optometrists have suboptimal performance for the nuanced diagnosis of glaucoma using fundus photographs alone. Enhanced training with diverse populations for AI is essential for improving GON detection and addressing the significant challenge of undiagnosed cases. [ABSTRACT FROM AUTHOR]

Purpose Until recently, limited options existed for patients with advanced melanoma who experienced disease progression while receiving treatment with ipilimumab. Here, we report the coprimary overall survival (OS) end point of CheckMate 037, which has previously shown that nivolumab resulted in more patients achieving an objective response compared with chemotherapy regimens in ipilimumab-refractory patients with advanced melanoma. Patients and Methods Patients were stratified by programmed death-ligand 1 expression, BRAF status, and best prior cytotoxic T-lymphocyte antigen-4 therapy response, then randomly assigned 2:1 to nivolumab 3 mg/kg intravenously every 2 weeks or investigator's choice chemotherapy (ICC; dacarbazine 1,000 mg/m2 every 3 weeks or carboplatin area under the curve 6 plus paclitaxel 175 mg/m2 every 3 weeks). Patients were treated until they experienced progression or unacceptable toxicity, with follow-up of approximately 2 years. Results Two hundred seventy-two patients were randomly assigned to nivolumab (99% treated) and 133 to ICC (77% treated). More nivolumab-treated patients had brain metastases (20% v 14%) and increased lactate dehydrogenase levels (52% v 38%) at baseline; 41% of patients treated with ICC versus 11% of patients treated with nivolumab received anti-programmed death 1 agents after randomly assigned therapy. Median OS was 16 months for nivolumab versus 14 months for ICC (hazard ratio, 0.95; 95.54% CI, 0.73 to 1.24); median progression-free survival was 3.1 months versus 3.7 months, respectively (hazard ratio, 1.0; 95.1% CI, 0.78 to 1.436). Overall response rate (27% v 10%) and median duration of response (32 months v 13 months) were notably higher for nivolumab versus ICC. Fewer grade 3 and 4 treatment-related adverse events were observed in patients on nivolumab (14% v 34%). Conclusion Nivolumab demonstrated higher, more durable responses but no difference in survival compared with ICC. OS should be interpreted with caution as it was likely impacted by an increased dropout rate before treatment, which led to crossover therapy in the ICC group, and by an increased proportion of patients in the nivolumab group with poor prognostic factors.

This article responds to a letter proposing a checklist approach for clinicians to determine whether an artificial intelligence (AI) algorithm is ready for use in medical practice. The checklist is designed to help clinicians evaluate the potential benefits and limitations of proposed algorithms, including their effectiveness, transparency, and generalizability. The article suggests that ethical implications, regulatory oversight, cybersecurity, data ownership, patient autonomy, and informed consent should also be considered when using AI in medical practice. The authors conclude that while AI algorithms for visual field analysis are still in their infancy, clinicians should carefully evaluate the evidence and potential risks and benefits before implementing them in their practice. [Extracted from the article]

Competing Interests: S. Schrittenlocher, V. Lüke, H. Irle, J. Weliwitage, J.N. Lüke, A. Lappas, T. Dietlein und C. Cursiefen, dass kein Interessenkonflikt besteht. G. Kong und A.J. Vingrys sind Gründer (Founding Director) und CSO beim Glance Optical Pty., Ltd. und besitzen Anteile an dem Melbourne Rapid Fields Applikation.

Aim and Objective: Developing improved methods for early detection of visual field defects is pivotal to reducing glaucoma-related vision loss. The Melbourne Rapid Fields screening module (MRF-S) is an iPad-based test, which allows suprathreshold screening with zone-based analysis to rapidly assess the risk of manifest glaucoma. The versatility of MRF-S has potential utility in rural areas and during infectious pandemics. This study evaluates the utility of MRF-S for detecting field defects in non-metropolitan settings., Materials and Methods: This was a prospective, multicenter, cross-sectional validation study. Two hundred and fifty-two eyes of 142 participants were recruited from rural sites through two outreach eye services in Australia. Participants were tested using MRF-S and compared with a reference standard; either Zeiss Humphrey Field Analyzer or Haag-Streit Octopus performed at the same visit. Standardized questionnaires were used to assess user acceptability. Major outcome measures were the area under the curve (AUC) for detecting mild and moderate field defects defined by the reference tests, along with corresponding performance characteristics (sensitivity, specificity)., Results: The mean test duration for MRF-S was 1.88 minutes compared with 5.92 minutes for reference tests. The AUCs for mild and moderate field defects were 0.81 [95% confidence interval (CI): 0.75-0.87] and 0.87 (95% CI: 0.83-0.92), respectively, indicating very good diagnostic accuracy. Using a risk criterion of 55%, MRF-S identified moderate field defects with a sensitivity and specificity of 88.4 and 81.0%, respectively., Conclusion and Clinical Significance: The MRF-S iPad module can identify patients with mild and moderate field defects while delivering favorable user acceptability and short test duration. This has potential application within rural locations and amidst infectious pandemics., How to Cite This Article: Chia MA, Trang E, Agar A, et al. Screening for Glaucomatous Visual Field Defects in Rural Australia with an iPad. J Curr Glaucoma Pract 2021;15(3):125-131., Competing Interests: Source of support: George Kong received grant funding from the Ophthalmic Research Institute of Australia, Royal Victorian Eye and Ear Hospital (East Melbourne VIC 3002), and Glaucoma Australia (Artarmon NSW 2064) Conflict of interest: George Kong and Algis Vingrys are Founding Directors of Glance Optical Pty. Ltd., the maker of the Melbourne Rapid Fields software, (Copyright © 2021; The Author(s).)