Purpose To investigate the comparative efficacy of intense pulsed light (IPL) therapy alone with that of IPL plus meibomian gland expression (MGX) for meibomian gland dysfunction (MGD). Methods This is a prospective randomized crossover clinical trial. Sixty patients were enrolled and randomly assigned to two groups. All of patients underwent four treatment sessions in total, which were two weeks apart. Group 1 underwent two sessions of IPL therapy with MGX, as well as two sessions of IPL alone. Group 2 received two sessions of IPL therapy alone, and two sessions of IPL therapy with MGX. The following parameters were measured at baseline (BL), 2 weeks after the second treatment session (FU1), and 2 weeks after the fourth treatment session (FU2): tearfilm break-up time (BUT), Oxford grade for corneal staining, meibomian gland expressibility (MGE), meibum quality (MQ), and ocular surface disease index (OSDI). The separate effect of MGX on improvement of MGD parameters was evaluated using generalized estimating equation (GEE). Results The mean age of the participants was 57.52 ± 10.50 years. The BUT, Oxford grade, MGE, MQ, and OSDI of both groups improved significantly (from baseline) by the end of four treatment sessions (FU2 compared to BL; all p-values <0.05). The MGE and MQ significantly improved after the first and second treatment sessions (FU1 compare to BL; all p-values < 0.001). However, the improvement was not statistically significant after the third and fourth treatment sessions (FU2 compared to FU1; p-value of 0.388 for MGE and 0.645 for MQ in group 1, 0.333 for MGE and 0.333 for MQ in group 2). The IPL plus MGX therapy produced greater improvements in the BUT scores than did IPL therapy alone (p = 0.003 by GEE). In contrast, the Oxford grade, MGE, MQ, and OSDI were not influenced by the addition of MGX to IPL (p = 0.642, 0.663, 0.731, and 0.840, respectively by GEE). Conclusion IPL therapy effectively improves the subjective symptoms and objective ocular findings of MGD. MGX enhanced the improvement of BUT driven by IPL therapy. The meibomian gland function (MGE and MQ) recovers faster in response to IPL therapy than did the other parameters.

Purpose To investigate the impact of the metabolic syndrome (METS) on the incidence of retinal vein occlusion (RVO). Methods This is a retrospective cohort study using Korean National Health Insurance System data. 23,153,600 subjects without previous history of RVO underwent a National Health Screening Program examination between 2009 and 2012. They were monitored for RVO development (registration of diagnostic code for RVO) until 2015. Presence of METS was defined using the data from the National Health Screening Program examination according to the revised criteria of the National Cholesterol Education Program Adult Treatment Panel III. A multivariate adjusted Cox regression analysis was used to reveal hazard ratios and 95% confidence interval for RVO development in the presence of METS. Results The age of the subjects was 47.64 ± 13.51 years. In this cohort, 11,747,439 (50.7%) were male, 11,406,161 (49.3%) were female, and 6,398,071 subjects (27.6%) were diagnosed with METS. The overall incidence of RVO was 0.947 per 1000 person-years. The adjusted hazard ratio of RVO in the presence of METS was 1.458 (95% confidence interval, 1.440–1.475; P < 0.001) after adjusting for age, sex, smoking status, alcohol consumption, physical activity, and income. Among all of the criteria for METS diagnosis, elevated blood pressure was the greatest risk for RVO development (adjusted hazard ratio, 1.610; 95% confidence interval, 1.589–1.631; P < 0.001). Conclusions METS and each of diagnostic criteria was associated with an increased risk of RVO development. Elevated blood pressure seems to be especially important factors for RVO development. Translational Relevance Our results provide information about the link between METS and RVO.

This study tried to compare the clinical outcomes of femtosecond laser-assisted astigmatic keratotomy (FSAK) and toric intraocular lens (IOL) implantation for astigmatism correction and identify factors affecting the efficacy of FSAK and toric IOL implantation in astigmatism correction. This retrospective case series comprised patients with corneal astigmatism ranging between 0.5 D and 4.5 D. Patients underwent FSAK or toric IOL implantation for cataract treatment and correction of astigmatism at the Samsung Medical Center, a tertiary surgical center, between April 2016 and December 2018. All patients underwent examination before and at three months after the surgery for comparative evaluation of refractive astigmatism, corneal high order aberrations and irregularity index. The astigmatism correction was analyzed by the Alpins method. Subgroup analysis of preoperative factors was based on the extent of target-induced astigmatism (TIA), the degree of astigmatism, and astigmatism classification based on topography. Thirty-one eyes underwent toric IOL implantation and 35 eyes underwent FSAK. The refractive astigmatism was significantly decreased in both toric IOL (P = 0.000) and FSAK group (P = 0.003). The correction index (CI) of refractive astigmatism was 0.84 ± 0.39 in the toric IOL and 0.71 ± 0.60 in the FSAK group. There was no difference between the two groups (P = 0.337). The CI of the FSAK group was significantly lower than in the toric IOL group when TIA was more than 1.5 D (P = 0.006), when correcting against-the-rule (P = 0.017), and limbus-to-limbus astigmatism (P = 0.008). In conclusion, toric IOL implantation is an effective and safe procedure for correcting preoperative astigmatism in cataract surgery in the short-term observation.

Background Accurately predicting refractive error in children is crucial for detecting amblyopia, which can lead to permanent visual impairment, but is potentially curable if detected early. Various tools have been adopted to more easily screen a large number of patients for amblyopia risk. Objective For efficient screening, easy access to screening tools and an accurate prediction algorithm are the most important factors. In this study, we developed an automated deep learning–based system to predict the range of refractive error in children (mean age 4.32 years, SD 1.87 years) using 305 eccentric photorefraction images captured with a smartphone. Methods Photorefraction images were divided into seven classes according to their spherical values as measured by cycloplegic refraction. Results The trained deep learning model had an overall accuracy of 81.6%, with the following accuracies for each refractive error class: 80.0% for ≤−5.0 diopters (D), 77.8% for >−5.0 D and ≤−3.0 D, 82.0% for >−3.0 D and ≤−0.5 D, 83.3% for >−0.5 D and <+0.5 D, 82.8% for ≥+0.5 D and <+3.0 D, 79.3% for ≥+3.0 D and <+5.0 D, and 75.0% for ≥+5.0 D. These results indicate that our deep learning–based system performed sufficiently accurately. Conclusions This study demonstrated the potential of precise smartphone-based prediction systems for refractive error using deep learning and further yielded a robust collection of pediatric photorefraction images.