Purpose.: To assess the effects of NDM from cranberries on Staphylococcus epidermidis biofilm formed on soft contact lenses. Methods.: Soft contact lenses were incubated in Tryptic Soy Broth (TSB) together with S. epidermidis (ATCC35984/RP62A) and various concentrations of NDM, and inspected by scanning electron and confocal microscopy. The TSB was collected after sonification and monitored turbidometrically. Results.: NDM at ≥500 μg/mL concentration caused a significant (P < 0.01) reduction of biofilm. Scanning electron microscopy of biofilm in the presence of 500 to 1000 μg/mL NDM confirmed these results. In control lenses, multilayered mushroom-shaped biofilm and complete coverage of the lens surface were seen, whereas after incubation with 500 μg NDM per mL TSB, the biofilm was thinner with smaller protuberances, and exposed lens surface was partially seen. In samples incubated with 1000 μg NDM per mL TSB, the lens surface was clearly seen between sporadic microcolonies. Conclusions.: NDM reduces formation of biofilm on soft contact lenses. This has important implications for the prevention of contact lens–related corneal infections caused by S. epidermidis.

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, is often induced by dermatological medications, including corticosteroids, isotretinoin, and tetracyclines. Early detection is crucial for preventing irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may enable the automated and accurate detection of papilledema from fundus images, thereby supporting timely diagnosis and management. Objective: The primary objective of this study was to explore the diagnostic capability of ChatGPT-4o, a general large language model with multimodal input, in identifying papilledema from fundus photographs. For context, its performance was compared with a ResNet-based convolutional neural network (CNN) specifically fine-tuned for ophthalmic imaging, as well as with the assessments of two human ophthalmologists. The focus was on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1094 fundus images (295 papilledema, 799 normal) was preprocessed and partitioned into a training set and a test set. The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. GPT-4o and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa, were calculated for each evaluator. Results: GPT-4o, when applied to papilledema detection, achieved an overall accuracy of 85.9% with substantial agreement beyond chance (Cohen’s Kappa = 0.72), but lower specificity (78.9%) and positive predictive value (73.7%) compared to benchmark models. For context, the ResNet model, fine-tuned for ophthalmic imaging, reached near-perfect accuracy (99.5%, Kappa = 0.99), while two human ophthalmologists achieved accuracies of 96.0% (Kappa ≈ 0.92). Conclusions: This study explored the capability of GPT-4o, a large language model with multimodal input, for detecting papilledema from fundus photographs. GPT-4o achieved moderate diagnostic accuracy and substantial agreement with the ground truth, but it underperformed compared to both a domain-specific ResNet model and human ophthalmologists. These findings underscore the distinction between generalist large language models and specialized diagnostic AI: while GPT-4o is not optimized for ophthalmic imaging, its accessibility, adaptability, and rapid evolution highlight its potential as a future adjunct in clinical screening, particularly in underserved settings. These findings also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented.

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, can be related to dermatological medications including corticosteroids, isotretinoin, and tetracyclines. Early detection is critical to prevent irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may offer a potential for automated, accurate papilledema detection from fundus images, supporting timely diagnosis and management. Objective: Evaluating and comparing the diagnostic performance of two AI models, including a ResNet-based convolutional neural network (CNN) and GPT-4, against human ophthalmologists in identifying papilledema from fundus photographs, with a focus on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1,389 fundus images (295 papilledema, 295 pseudo-papilledema, 799 normal) was preprocessed and partitioned into a test set of 198 images (99 papilledema, 99 normal). The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. Both AI models and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics such as sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa were calculated for each evaluator. Results: All human evaluators demonstrated high sensitivity for papilledema detection (96.91%–99.0%). The ResNet AI model achieved the highest specificity (100%), PPV (100%), NPV (98.99%), and overall accuracy (99.49%), outperforming both human evaluators (accuracy: 95.96%) and GPT-4 (accuracy: 85.86%, specificity: 78.86%, PPV: 73.74%). Cohen’s Kappa indicated excellent agreement for the ResNet model (0.9899) and human experts (0.9192), with substantial agreement for GPT-4 (0.71). Conclusions: These results highlight the potential of AI-assisted detection of papilledema in underserved settings. However, they also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented.