may be unnecessary in patients for whom immediate macular hole closure can be documented intraoperatively by OCT. This may be an important patient satisfaction consideration for surgery.Macular holes have been shown to close either by simple closure or by bridging inner retinal tissue with slow reabsorption of residual foveal subretinal fluid. 15 The mechanisms of these differences in closure are unclear but could be related to the health of the subfoveal retinal pigment epithelium. The patient reported here developed bridging tissue on intraoperative OCT with subretinal fluid reabsorption within 2 weeks. This case demonstrates the potential of intraoperative OCT as a powerful tool to elucidate the mechanisms of successful macular hole repair and guide our clinical decision making.

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Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. This study aimed to evaluate the performance of a deep learning model in differentiating eyelid lesions using clinical eyelid photographs in comparison with human ophthalmologists. We included 4954 photographs from 928 patients in this retrospective cross-sectional study. Images were classified into three categories: malignant lesion, benign lesion, and no lesion. Two pre-trained convolutional neural network (CNN) models, DenseNet-161 and EfficientNetV2-M architectures, were fine-tuned to classify images into three or two (malignant versus benign) categories. For a ternary classification, the mean diagnostic accuracies of the CNNs were 82.1% and 83.0% using DenseNet-161 and EfficientNetV2-M, respectively, which were inferior to those of the nine clinicians (87.0–89.5%). For the binary classification, the mean accuracies were 87.5% and 92.5% using DenseNet-161 and EfficientNetV2-M models, which was similar to that of the clinicians (85.8–90.0%). The mean AUC of the two CNN models was 0.908 and 0.950, respectively. Gradient-weighted class activation map successfully highlighted the eyelid tumors on clinical photographs. Deep learning models showed a promising performance in discriminating malignant versus benign eyelid lesions on clinical photographs, reaching the level of human observers.