Genome-wide association study of neovascular age-related macular degeneration in the Thai population.

We performed a genome-wide association study on 377 cases of neovascular age-related macular degeneration (AMD) and 1074 controls to determine the association of previously reported genetic variants associated with neovascular AMD in the Thai population. All patients were of Thai ancestry. We confirmed the association of age-related maculopathy susceptibility 2 (ARMS2) rs10490924 (P=7.38 × 10-17), HTRA1 rs11200638 (P=5.47 × 10-17) and complement factor H gene (CFH) rs800292 (P=2.53 × 10-8) with neovascular AMD, all loci passing the genome-wide significance level (P<5.22 × 10-8). We also found association of the previously reported CFH rs10737680 (P=1.76 × 10-6) locus in the discovery sample. Two loci not previously reported to be associated with neovascular AMD were selected for replication in 222 cases and 623 controls. The loci included LINCO1317 rs6733379 and rs2384550 on chromosome 12. LINCO1317 rs6733379 (P=3.85 × 10-2) remained significantly associated with neovascular AMD after replication. In conclusion, we confirm that ARMS2, HTRA1 and CFH variants are associated with neovascular AMD in the Thai population. Findings from this study also suggest that variants contributing to the susceptibility of neovascular AMD in the Thai population are mostly similar to other Asians with additional local genetic risks that may specifically be identified in Thai population.

Rare genetic variation in VE-PTP is associated with central serous chorioretinopathy, venous dysfunction and glaucoma.

Central serous chorioretinopathy (CSC) is a fluid maculopathy whose etiology is not well understood. Abnormal choroidal veins in CSC patients have been shown to have similarities with varicose veins. To identify potential mechanisms, we analyzed genotype data from 1,477 CSC patients and 455,449 controls in FinnGen. We identified an association for a low-frequency (AF=0.5%) missense variant (rs113791087) in the gene encoding vascular endothelial protein tyrosine phosphatase (VE-PTP) (OR=2.85, P=4.5×10-9). This was confirmed in a meta-analysis of 2,452 CSC patients and 865,767 controls from 4 studies (OR=3.06, P=7.4×10-15). Rs113791087 was associated with a 56% higher prevalence of retinal abnormalities (35.3% vs 22.6%, P=8.0×10-4) in 708 UK Biobank participants and, surprisingly, with varicose veins (OR=1.31, P=2.3×10-11) and glaucoma (OR=0.82, P=6.9×10-9). Predicted loss-of-function variants in VEPTP, though rare in number, were associated with CSC in All of Us (OR=17.10, P=0.018). These findings highlight the significance of VE-PTP in diverse ocular and systemic vascular diseases.

Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment.

Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss. In this article, we review the use of AI for DR screening on color retinal photographs in different phases of application, ranging from development to deployment. Early studies of machine learning (ML)-based algorithms using feature extraction to detect DR achieved a high sensitivity but relatively lower specificity. Robust sensitivity and specificity were achieved with the application of deep learning (DL), although ML is still used in some tasks. Public datasets were utilized in retrospective validations of the developmental phases in most algorithms, which require a large number of photographs. Large prospective clinical validation studies led to the approval of DL for autonomous screening of DR although the semi-autonomous approach may be preferable in some real-world settings. There have been few reports on real-world implementations of DL for DR screening. It is possible that AI may improve some real-world indicators for eye care in DR, such as increased screening uptake and referral adherence, but this has not been proven. The challenges in deployment may include workflow issues, such as mydriasis to lower ungradable cases; technical issues, such as integration into electronic health record systems and integration into existing camera systems; ethical issues, such as data privacy and security; acceptance of personnel and patients; and health-economic issues, such as the need to conduct health economic evaluations of using AI in the context of the country. The deployment of AI for DR screening should follow the governance model for AI in healthcare which outlines four main components: fairness, transparency, trustworthiness, and accountability.

Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD. The PubMed database was searched to identify studies for review. Ocular biomarkers in AD research and AI research on AD were reviewed and summarized. According to numerous studies, there is a high likelihood that ocular biomarkers can be used to detect early AD: tears, corneal nerves, retina, visual function and, in particular, eye movement tracking have been identified as ocular biomarkers with the potential to detect early AD. However, there is currently no ocular biomarker that can be used to definitely detect early AD. A few studies that used AI with ocular biomarkers to detect AD reported promising results, demonstrating that using AI with ocular biomarkers through multimodal imaging could improve the accuracy of identifying AD patients. This strategy may become a screening tool for detecting early AD in older patients prior to the onset of AD symptoms.

Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic.

OBJECTIVES: Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public's practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK. METHODS: After validation, AiMASK collected data from 32 districts in Bangkok. We analyzed the association between factors affecting the unprotected group (incorrect or non-mask wearing) using univariate logistic regression analysis. RESULTS: AiMASK was validated before data collection with accuracy of 97.83% and 91% during internal and external validation, respectively. AiMASK detected a total of 1,124,524 people. The unprotected group consisted of 2.06% of incorrect mask-wearing group and 1.96% of non-mask wearing group. Moderate negative correlation was found between the number of COVID-19 patients and the proportion of unprotected people (r = -0.507, p<0.001). People were 1.15 times more likely to be unprotected during the holidays and in the evening, than on working days and in the morning (OR = 1.15, 95% CI 1.13-1.17, p<0.001). CONCLUSIONS: AiMASK was as effective as human graders in detecting face mask wearing. The prevailing number of COVID-19 infections affected people's mask-wearing behavior. Higher tendencies towards no protection were found in the evenings, during holidays, and in city centers.

The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation.

We compared the performance of deep learning (DL) in the classification of optical coherence tomography (OCT) images of macular diseases between automated classification alone and in combination with automated segmentation. OCT images were collected from patients with neovascular age-related macular degeneration, polypoidal choroidal vasculopathy, diabetic macular edema, retinal vein occlusion, cystoid macular edema in Irvine-Gass syndrome, and other macular diseases, along with the normal fellow eyes. A total of 14,327 OCT images were used to train DL models. Three experiments were conducted: classification alone (CA), use of automated segmentation of the OCT images by RelayNet, and the graph-cut technique before the classification (combination method 1 (CM1) and 2 (CM2), respectively). For validation of classification of the macular diseases, the sensitivity, specificity, and accuracy of CA were found at 62.55%, 95.16%, and 93.14%, respectively, whereas the sensitivity, specificity, and accuracy of CM1 were found at 72.90%, 96.20%, and 93.92%, respectively, and of CM2 at 71.36%, 96.42%, and 94.80%, respectively. The accuracy of CM2 was statistically higher than that of CA (p = 0.05878). All three methods achieved AUC at 97%. Applying DL for segmentation of OCT images prior to classification of the images by another DL model may improve the performance of the classification.

Open Globe Injuries From Garage Door Springs.

We describe seven patients who were attempting to repair their garage door when a spring dislodged at high velocity, resulting in open globe injury. All patients were seen at Massachusetts Eye and Ear between the years 2008 and 2023. Their final visual acuities ranged from 20/125 to no light perception. Open globe injury appears to be a risk of attempts to repair a garage door by people who are inexperienced in doing so. [Ophthalmic Surg Lasers Imaging Retina 2023;54:666-669.].

Characteristics of road traffic mortality and distribution of healthcare resources in Thailand.

Road traffic mortalities (RTMs), a leading cause of death globally, mostly occur in low- and middle-income countries, and having sufficient healthcare resources could lower the number of these fatalities. Our study aimed to illustrate the incidence of RTMs per 100,000 population and to compare the distribution of healthcare resources from 2011 to 2021 with rates of RTMs in the 77 provinces of Thailand. We divided the population into adults (≥ 15 years) and children (0-14 years). Lorenz curve and Gini coefficient were used to measure the level of distribution and equality of hospital resources and in relation to RTMs across the country. The average number of deaths was 30.34 per 100,000 per year, with male predominance. The RTM rates for adults and children were 32.71 and 19.08 per 100,000 respectively, and motorcycle accidents accounted for the largest percentage of deaths across all age groups. The Gini coefficient showed that operating rooms (0.42) were the least equally distributed hospital resource, while physicians were the most equally distributed (0.34). Anomalies were identified between the distribution of RTMs and available hospital resources. We hope our study will be beneficial in reallocating these resources more fairly to reflect the different numbers of traffic accidents in each province with the aim of reducing lower traffic-related deaths.

Effects of digital devices and online learning on computer vision syndrome in students during the COVID-19 era: an online questionnaire study.

PURPOSE: Computer vision syndrome (CVS) describes a group of eye and vision-related problems that result from prolonged digital device use. This study aims to assess the prevalence and associated factors of CVS among students during the lockdown resulting from the COVID-19 pandemic. METHODS: A cross-sectional, online, questionnaire-based study performed among high school students in Thailand. RESULTS: A total of 2476 students, with mean age of 15.52±1.66 years, were included in this study. The mean number of hours of digital device use per day (10.53±2.99) increased during the COVID-19 pandemic compared with before its advent (6.13±2.8). The mean number of hours of online learning was 7.03±2.06 hours per day during the pandemic. CVS was found in 70.1% of students, and its severity correlated with both the number of hours of online learning and the total number of hours of digital device usage (p<0.001). Multiple logistic regression analysis revealed that the factors associated with CVS included age ≤15 years (adjusted OR (AOR)=2.17), overall digital device usage >6 hours per day (AOR=1.91), online learning >5 hours per day (AOR=4.99), multiple digital device usage (AOR=2.15), refractive errors (AOR=2.89), presence of back pain (AOR=2.06) and presence of neck pain (AOR=2.36). CONCLUSIONS: The number of hours of digital device usage increased during lockdown. Over 70% of children had CVS, whose associated factors, including hours of digital device usage, hours of online learning, ergonomics and refractive errors, should be adjusted to decrease the risk of acquiring this condition. Online learning will remain, along with CVS, after this pandemic, and we hope our research will be taken into account in remodelling our education system accordingly.

Risk factors for endophthalmitis after cataract surgery in diabetic patients: a case control study.

AIM: To identify risk factors associated with post-cataract surgery endophthalmitis (PCE) in type 2 diabetic patients. METHODS: A hospital-based retrospective case-control study was conducted on 194 type 2 diabetic patients undergoing cataract surgery in Rajavithi Hospital from January 2007 to December 2015. Fifteen patients with PCE were included as the case group and 179 patients without PCE were included as the control group. Potential factors associated with PCE among both groups including demographics, pre-operative characteristics, surgical settings and complications, were statistically analyzed using Chi-square testing and a logistic regression model. RESULTS: Within the case group, 53% were females and the median age was 68y. Univariate analysis of pre-operative characteristics, surgical settings and complications revealed that recent pre-operative fasting plasma glucose, insulin therapy, presence of diabetic retinopathy, and severe non-proliferative or proliferative diabetic retinopathy were significantly associated with PCE. In a multivariate analysis adjusting for blood glucose level, insulin treatment was the only significant factor associated with an increased risk of PCE (OR 3.9, 95%CI 1.0-15.0, P=0.04) compared to patients without insulin treatment. The most common causative organisms were gram-positive bacteria (89%). Staphylococcus species represented the most common group (67%). Median best corrected visual acuity at 1-month and 3-month follow-up was equal at 0.7 logMAR (20/100). CONCLUSION: The authors identify insulin treatment as the only risk factor associated with endophthalmitis after cataract surgery in type 2 diabetic patients. Further studies with serum levels of pre-operative glycated hemoglobin (HbA1c) and post-operative fasting plasma glucose level are essential to truly demonstrate the role of peri-operative glycemic markers as a risk factor for PCE.