Visual acuity can be improved with perceptual learning and patching in older children and adult patients with anisometropic amblyopia. The improvements in visual acuity achieved with patching were one line better than those achieved with perceptual learning. Perceptual learning might provide an alternative treatment in patients with anisometropic amblyopia.

This study is aimed to determine the relationship between orbital fracture sites in each CT scan view and postoperative diplopia. Data for 141 patients of orbital wall fracture were analyzed retrospectively. One group of examiners reviewed sagittal, coronal and axial CT scans. Descriptive statistical analysis was used to assess each fracture area and its potential relationship with the occurrence of postoperative diplopia. Among the three anatomical views, sagittal sections were significantly associated with post-operative diplopia (PD) (p = 0.044). For orbital wall fractures in a single location, C1 (p = 0.015), A1 (p = 0.004) and S3 (p = 0.006) fractures were significantly related to PD. Orbital wall fractures found in more than one location resulted in a higher probability of PD in all sections:, C1 + C2 group (p = 0.010), C1 + C2 + C3 group (p = 0.005), A1 + A2 group (p = 0.034), A3 + A1 group (p = 0.005), S1 + S2 group (p < 0.001), S2 + S3 group (p = 0.006) and S1 + S2 + S3 group (p < 0.001). For combinations of two or three sections, we found that only fractures involving both coronal and sagittal sections led to a significantly increased risk of PD (p = 0.031). PD is the main posttreatment complication of orbital bone fracture reduction. In addition to the known myogenic cause (failure to relieve entrapment) of diplopia, both trauma and surgical manipulation can compromise ocular motor nerve function and possibly result in the development of neurogenic causes of diplopia. Careful assessment of patient symptoms (whether preoperative diplopia is present), and the location of orbital fractures (and the influence of related musculature, fat, and nerves) on CT scans are strongly related to surgical success.

Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.