Both the cross-like and circular Nd:YAG laser capsulotomy techniques induce similar visual and IOP changes. The circular technique is associated with a higher amount of energy used, more floater symptoms, and has a greater effect on macular thickness at one day after laser capsulotomy.

Purpose To evaluate the refractive precision of three‐piece versus one‐piece intraocular lenses (IOLs) in eyes with pseudoexfoliation (PXF) and changes in anterior chamber depth (ACD) following cataract surgery. Methods This prospective, nonrandomized, interventional, comparative case series included PXF eyes, which were implanted with one‐piece acrylic IOL or three‐piece acrylic IOLs (Group A; n = 31, Group B; n = 30). Non‐PXF control eyes were implanted with one‐piece acrylic IOL (Group C; n = 30). Postoperative refractive error (RE) and absolute RE, as well as pre‐ to postoperative ACD changes, were statistically analysed. A p value < 0.05 was considered statistically significant. Results Changes in ACD (mean ± standard deviation) in Groups A, B and C were 0.9 ± 0.4, 1.0 ± 0.3 and 0.7 ± 0.4 mm, respectively. Significant differences were found between Groups A and C (p = 0.043) and between Groups B and C (p = 0.008). In Groups A, B and C, the median and interquartile (Q1 to Q3) values were 0.3 (−0.8 to 1.0), −0.5 (−0.8 to −0.3) and 0.1 (−0.4 to 0.3) for RE and 1.0 (0.4 to 1.3), 0.5 (0.3 to 0.8) and 0.3 (0.3 to 0.5) for absolute RE, respectively. The RE differences between Group A and B (p = 0.009) and Group B and C (p < 0.001) were statistically significant. For absolute RE, the differences were significant for all comparisons (p < 0.05). Conclusion Refractive precision in PXF eyes may be better with three‐piece than with one‐piece IOL implantation, but worse than with one‐piece IOL implantation in non‐PXF eyes. Significant changes in ACD in PXF eyes may be related to RE.

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This study aims to analyze the effectiveness of the internet-based, free Teachable Machine (TM) platform, which does not entail code knowledge, in detecting the presence and types of strabismus in the optimum hyperparameters. Material and Methods:The images obtained from the patients who presented to our clinic with the complaint of ocular deviation were analyzed, and 523 [176 esotropia (ET), 195 exotropia (XT), and 152 orthophoria (ORTHO)] images were included in this study. After the images were uploaded to the TM platform, 6 different batch sizes and 9 different learning rates were tested using the grid search method, with the number of epochs fixed at 4,000 to determine the optimum hyperparameter. Results: The highest overall test accuracy was 0.887, and the hyperparameters from which this accuracy was obtained were 200 for the number of epochs, 256 for the batch size, and 0.0005 for the learning rate. In the TM model trained with these parameters, accuracy values of ET: 0.96, ORTHO: 0.78 and XT: 0.9 were obtained in the subgroups, respectively. Conclusion: To achieve optimal accuracy at the stage of development of the artificial intelligence model, users should determine the appropriate hyperparameter values depending on the size of the available dataset and the complexity of the data. The results we obtained by determining the optimum hyperparameters have revealed that the presence of strabismus can be detected with high accuracy using TM, an internet-based, free deep learning platform that does not entail having code knowledge.