Posterior segment manifestations of Takayasu arteritis: A narrative review

Ocular symptoms can be the presenting manifestation of Takayasu arteritis (TA) or could be indicative of disease reactivation. A review of published literature related to posterior segment manifestations of TA by using the keywords "Takayasu arteritis," "ophthalmic manifestations," "retina," "retinopathy," "ocular," "optic nerve," and "optic neuropathy" was performed. In total, 62 case reports and 12 case series were included. The majority of the articles were from Asia (n = 47, 64%). Females outnumbered males in the ratio of 7:1. The mean age of patients was 33 years (range: 8-78 years, SD: 13.5 years). In 58% (n = 41 out of 71) cases, ocular symptoms were the presenting manifestation of the underlying disease. Hypotensive retinopathy was found in 70% of eyes, and hypertensive retinopathy was found in 27%. The mean presenting visual acuity (VA) was +1.03 logMAR (range: -0.12 to 3, SD: 1.07), and at the final follow-up was +1.02 logMAR (range: -0.12 to 3, SD 1.17). VA improved in 34% (n = 29/86), remained stable in 45% (39/86), and worsened in 21% (18/86). The mean follow-up was 9 months (range: 0.5-204, SD: 16 months).

Automatic Identification and Severity Classification of Retinal Biomarkers in SD-OCT Using Dilated Depthwise Separable Convolution ResNet with SVM Classifier

PURPOSE

Diagnosis of Uveitic Macular Edema (UME) using Spectral Domain OCT (SD-OCT) is a promising method for early detection and monitoring of sight-threatening visual impairment. Viewing multiple B-scans and identifying biomarkers is challenging and time-consuming for clinical practitioners. To overcome these challenges, this paper proposes an image classification hybrid framework for predicting the presence of biomarkers such as intraretinal cysts (IRC), hyperreflective foci (HRF), hard exudates (HE) and neurosensory detachment (NSD) in OCT B-scans along with their severity.

METHODS

A dataset of 10880 B-scans from 85 Uveitic patients is collected and graded by two board-certified ophthalmologists for the presence of biomarkers. A novel image classification framework, Dilated Depthwise Separable Convolution ResNet (DDSC-RN) with SVM classifier, is developed to achieve network compression with a larger receptive field that captures both low and high-level features of the biomarkers without loss of classification accuracy. The severity level of each biomarker is predicted from the feature map, extracted by the proposed DDSC-RN network.

RESULTS

The proposed hybrid model is evaluated using ground truth labels from the hospital. The deep learning model initially, identified the presence of biomarkers in B-scans. It achieved an overall accuracy of 98.64%, which is comparable to the performance of other state-of-the-art models, such as DRN-C-42 and ResNet-34. The SVM classifier then predicted the severity of each biomarker, achieving an overall accuracy of 89.3%.

CONCLUSIONS

A new hybrid model accurately identifies four retinal biomarkers on a tissue map and predicts their severity. The model outperforms other methods for identifying multiple biomarkers in complex OCT B-scans. This helps clinicians to screen multiple B-scans of UME more effectively, leading to better treatment outcomes.

National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART-India cross-sectional study

BACKGROUND

National estimates of the prevalence of vision impairment and blindness in people with diabetes are required to inform resource allocation. People with diabetes are more susceptible to conditions such as diabetic retinopathy that can impair vision; however, these are often missed in national studies. This study aims to determine the prevalence and risk factors of vision impairment and blindness in people with diabetes in India.

METHODS

Data from the SMART-India study, a cross-sectional survey with national coverage of 42 147 Indian adults aged 40 years and older, collected using a complex sampling design, were used to obtain nationally representative estimates for the prevalence of vision impairment and blindness in people with diabetes in India. Vulnerable adults (primarily those who did not have capacity to provide consent); pregnant and breastfeeding women; anyone deemed too ill to be screened; those who did not provide consent; and people with type 1 diabetes, gestational diabetes, or secondary diabetes were excluded from the study. Vision impairment was defined as presenting visual acuity of 0·4 logMAR or higher and blindness as presenting a visual acuity of 1·0 logMAR or higher in the better-seeing eye. Demographic, anthropometric, and laboratory data along with geographic distribution were analysed in all participants with available data. Non-mydriatic retinal images were used to grade diabetic retinopathy, and risk factors were also assessed.

FINDINGS

A total of 7910 people with diabetes were included in the analysis, of whom 5689 had known diabetes and 2221 were undiagnosed. 4387 (55·5%) of 7909 participants with available sex data were female and 3522 (44·5%) participants were male. The estimated national prevalence of vision impairment was 21·1% (95% CI 15·7-27·7) and blindness 2·4% (1·7-3·4). A higher prevalence of any vision impairment (29·2% vs 19·6%; p=0·016) and blindness (6·7% vs 1·6%; p<0·0001) was observed in those with ungradable images. In known diabetes, diabetic retinopathy (adjusted odds ratio [aOR] 3·06 [95% CI 1·25-7·51]), vision-threatening diabetic retinopathy (aOR 7·21 [3·52-14·75]), and diabetic macular oedema (aOR 5·41 [2·20-13·33]) were associated with blindness in adjusted analysis. Common sociodemographic risk factors for vision impairment and blindness include older age, lower educational attainment, and unemployment.

INTERPRETATION

Based on the estimated 101 million people with diabetes in 2021 and the interpretation of the data from this study, approximately 21 million people with diabetes have vision impairment in India, of whom 2·4 million are blind. Higher prevalence is observed in those from lower socio-economic strata and policy makers should focus on these groups to reduce inequalities in health care.

FUNDING

Global Challenge Research Fund of United Kingdom Research and Innovation through the Medical Research Council.

Global warming impact of fluorinated gases in ophthalmic surgeries at a tertiary eye center in India

PURPOSE

Global warming is one of the greatest health threats of the 21 st century. The ophthalmic sector contributes to the emission of greenhouse gases, thus altering the natural environment. There is currently no data on global emissions of fluorinated gases in ophthalmic surgery. This retrospective study from 2017 to 2021 aims to report the carbon dioxide (CO 2 ) equivalence of sulfur hexafluoride (SF 6 ), hexafluoroethane (C 2 F 6 ), and octafluoropropane (C 3 F 8 ) at a tertiary eye center.

METHODS

Data collected from 1842 surgical procedures that used injections of fluorinated gases were analyzed. Environmental impact (global warming potential over 100 years) was calculated by converting milliliters to grams by using modified ideal gas law at standard temperature and pressure for the canisters and then to their CO 2 equivalence.

RESULTS

Though 70% of surgeries used C 3 F 8 , the least greenhouse effect causing fluorinated gas, the total carbon emission was 1.4 metric tons. The most common indication was macular hole surgery (36.86%).

CONCLUSION

This study paves a step toward analyzing the problem statement, thus awakening us to contemplate options to make ophthalmic surgeries greener.

Timing of Radiotherapy (RT) After Radical Prostatectomy (RP): Long-term outcomes in the RADICALS-RT trial [NCT00541047]

BACKGROUND

The optimal timing of radiotherapy (RT) after radical prostatectomy for prostate cancer has been uncertain. RADICALS-RT compared efficacy and safety of adjuvant RT versus an observation policy with salvage RT for PSA failure.

METHODS

RADICALS-RT was a randomised controlled trial enrolling patients with ≥1 risk factor (pT3/4, Gleason 7-10, positive margins, pre-op PSA≥10ng/ml) for recurrence after radical prostatectomy. Patients were randomised 1:1 to adjuvant RT ("Adjuvant-RT") or an observation policy with salvage RT for PSA failure ("Salvage-RT") defined as PSA≥0.1ng/ml or 3 consecutive rises. Stratification factors were Gleason score, margin status, planned RT schedule (52.5Gy/20 fractions or 66Gy/33 fractions) and treatment centre. The primary outcome measure was freedom-from-distant metastasis, designed with 80% power to detect an improvement from 90% with Salvage-RT (control) to 95% at 10yr with Adjuvant-RT. Secondary outcome measures were bPFS, freedom-from-non-protocol hormone therapy, safety and patient-reported outcomes. Standard survival analysis methods were used; HR<1 favours Adjuvant-RT.

FINDINGS

Between Oct-2007 and Dec-2016, 1396 participants from UK, Denmark, Canada and Ireland were randomised: 699 Salvage-RT, 697 Adjuvant-RT. Allocated groups were balanced with median age 65yr. 93% (649/697) Adjuvant-RT reported RT within 6m after randomisation; 39% (270/699) Salvage-RT reported RT during follow-up. Median follow-up was 7.8 years. With 80 distant metastasis events, 10yr FFDM was 93% for Adjuvant-RT and 90% for Salvage-RT: HR=0.68 (95%CI 0·43-1·07, p=0·095). Of 109 deaths, 17 were due to prostate cancer. Overall survival was not improved (HR=0.980, 95%CI 0.667-1.440, p=0.917). Adjuvant-RT reported worse urinary and faecal incontinence one year after randomisation (p=0.001); faecal incontinence remained significant after ten years (p=0.017).

INTERPRETATION

Long-term results from RADICALS-RT confirm adjuvant RT after radical prostatectomy increases the risk of urinary and bowel morbidity, but does not meaningfully improve disease control. An observation policy with salvage RT for PSA failure should be the current standard after radical prostatectomy.

Association of eGFR with stages of diabetic retinopathy and age-related macular degeneration in Indian population

PURPOSE

To investigate the influence of glomerular filtration rate in renal disease decline and its association with diabetic retinopathy (DR) and age-related macular degeneration (ARMD) in patients in South India.

METHODS

A population-based cross-sectional study was conducted including participants with DR and ARMD recruited from urban and rural populations. The data collection included medical history, anthropometric measurements, and ophthalmic work-up. The estimated glomerular filtration rate (eGFR) was calculated using the equation of chronic kidney disease-epidemiology collaboration (CKD-EPI). The grading of AMD was done by a single experienced (more than 5 years) vitreoretinal surgeon as per the International ARM Epidemiological Study Group and it was staged based on grading in the worsened eye.

RESULTS

A decline in eGFR was observed as the severity of DR increased (P < 0.001). Baseline characteristics such as age (P < 0.001), duration of diabetes (P < 0.001), gender (P < 0.001), creatinine (P < 0.001), albumin to creatinine ratio (ACR; P < 0.001), albuminuria (P = 0.023), blood urea (P < 0.001), and high-density lipoprotein (HDL; P = 0.003) were found to be statistically significant. The risk for developing DR with CKD was found to be 5 times higher in male patients compared to female patients. Age and high blood urea level, diastolic blood pressure, mild and moderate DR were the risk factors associated with CKD. A decline in eGFR was observed as the severity of ARMD increased (P < 0.001). The risk factors associated with CKD were age, gender, smoking, alcohol consumed, presence of hypertension, duration of diabetes, systolic and diastolic blood pressure, history of diabetes, body mass index (BMI), serum triglycerides, and serum HDL cholesterol.

CONCLUSION

Reduced eGFR values were associated with an increase in the severity of DR and ARMD.

Achievement of Target Gain Larger than Unity in an Inertial Fusion Experiment

On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain G_{target} of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven" (or G_{target}>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based on fundamental physics principles, is possible. This Letter reports on the target, laser, design, and experimental advancements that led to this result.

A deep learning system for predicting time to progression of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Natural course of non-center-involving diabetic macular edema progression in patients under initial observation

PURPOSE

We aim to report the natural course of non-center involving diabetic macular edema (NCIDME) progression to center involving diabetic macular edema (CIDME) and associated risk factors.

METHODS

This is a multicenter retrospective comparative study. Data was collected from electronic medical records from 8 centers in India covering. We included patients with type 2 diabetes above 18 years of age with treatment-naïve NCIDME on OCT and best-corrected visual acuity at baseline of 6/12 or better who were under observation for NCIDME and had 2 years follow-up data.

RESULTS

Out of 72 patients with NCIDME, 26.38% patients progressed to CI DME by 2 years, and the visit wise proportion was 11.11% at 6 months, 7% at 1st year and 8.3% at 2 years. The change in CST was statistically significant at 2 years in patients who developed CIDME, the mean difference was 137.73 ± 48.56 microns p = 0.045. Duration of diabetes mellitus > 10 years was the only risk factor for conversion to CIDME.

CONCLUSION

A quarter of eyes with NCIDME developed CIDME and 15% progressed from NPDR to PDR by 2 years, highlighting the disease burden in these patients with NCIDME.

Knowledge, attitude, and practice of diabetes in patients with and without sight-threatening diabetic retinopathy from two secondary eye care centres in India

BACKGROUND/AIMS

Good knowledge, Attitude, and Practice (KAP) of diabetes influence its control and complications. We examined the KAP of diabetes in patients with sight-threatening diabetic retinopathy (STDR) and non-sight-threatening diabetic retinopathy (NSTDR) attending two different referral hospitals in India.

METHODS

400 consecutive patients (mean age = 58.5 years ± 10.3) with diabetic retinopathy attending retina referral clinics in Chennai (private) and Darjeeling (public) were recruited. A validated questionnaire on diabetic KAP was administered in English or the local language. Data were analysed using an established scalar-scoring method in which a score of 1 was assigned to the correct answer/healthy lifestyle and 0 to an incorrect answer/unhealthy lifestyle/practice. Clinical data included fasting blood sugar, blood pressure, retinopathy, and visual acuity. Retinopathy was graded as STDR/NSTDR from retinal images using Early Treatment of Diabetic Retinopathy Study criteria.

RESULTS

Usable data from 383 participants (95.8%) were analysed. Of these, 83 (21.7%) had STDR, and 300 (78.3%) had NSTDR. The NSTDR group reported a significantly lower total KAP score (mean rank = 183.4) compared to the STDR group (mean rank = 233.1), z = -3.0, p < 0.001. A significantly greater percentage in the NSTDR group reported to being unaware that diabetes could affect eyes, did not know about possible treatment for DR, and checked their blood sugar less frequently than once a month.

CONCLUSION

Patients who had not developed STDR had poorer KAP about diabetes and diabetes-related eye diseases. This is an important issue to address as the risk of their progressing to STDR is high unless appropriate steps to improve their knowledge/awareness and lifestyle practice are introduced early.

Rationale and protocol paper for the Asia Pacific Network for inherited eye diseases

PURPOSE

There are major gaps in our knowledge of hereditary ocular conditions in the Asia-Pacific population, which comprises approximately 60% of the world's population. Therefore, a concerted regional effort is urgently needed to close this critical knowledge gap and apply precision medicine technology to improve the quality of lives of these patients in the Asia-Pacific region.

DESIGN

Multi-national, multi-center collaborative network.

METHODS

The Research Standing Committee of the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Society of Eye Genetics fostered this research collaboration, which brings together renowned institutions and experts for inherited eye diseases in the Asia-Pacific region. The immediate priority of the network will be inherited retinal diseases (IRDs), where there is a lack of detailed characterization of these conditions and in the number of established registries.

RESULTS

The network comprises 55 members from 35 centers, spanning 12 countries and regions, including Australia, China, India, Indonesia, Japan, South Korea, Malaysia, Nepal, Philippines, Singapore, Taiwan, and Thailand. The steering committee comprises ophthalmologists with experience in consortia for eye diseases in the Asia-Pacific region, leading ophthalmologists and vision scientists in the field of IRDs internationally, and ophthalmic geneticists.

CONCLUSIONS

The Asia Pacific Inherited Eye Disease (APIED) network aims to (1) improve genotyping capabilities and expertise to increase early and accurate genetic diagnosis of IRDs, (2) harmonise deep phenotyping practices and utilization of ontological terms, and (3) establish high-quality, multi-user, federated disease registries that will facilitate patient care, genetic counseling, and research of IRDs regionally and internationally.

Late postoperative vitreous cavity hemorrhage after vitrectomy for proliferative diabetic retinopathy-observation versus intervention

PURPOSE

To analyze the outcome of intervention versus observation for vitreous cavity hemorrhage occurring after a 2-month period of blood-free cavity (late postoperative vitreous cavity hemorrhage-POVCH) in eyes operated by vitrectomy for complications of proliferative diabetic retinopathy (PDR).

METHODS

This study was a 10-year retrospective, observational, multi-center study involving eight major vitreoretinal surgical centers across India from January 2010 to December 2019. The primary objective of the study was to assess the visual and clinical outcomes of various management approaches for late POVCH. The key secondary objective was to determine the best management option that prevented recurrence. Patients with follow-up of less than 6 months of POVCH management were excluded.

RESULTS

The occurrence of late POVCH was studied in 261 eyes. The median time to occurrence was 7 months (range: 2-87) postvitrectomy/silicone oil removal. The majority (58%) experienced a single, nonrecurring POVCH event. Visual acuity outcome was independent of all management approaches (P = 0.179; mean follow-up 20.7 ± 14.1 months). With watchful observation, spontaneous resolution was noted in 83% (60/72 eyes) of eyes in 81.5 days (interquartile range, 169.75). Silicone oil injection was most effective in preventing recurrence (P < 0.001).

CONCLUSION

The current treatment practice of late POVCH management in PDR suggests that watchful observation for at least 3 months could be as efficacious as any surgical intervention.

Is Kidney Function Associated with Age-Related Macular Degeneration?: Findings from the Asian Eye Epidemiology Consortium

PURPOSE

Chronic kidney disease (CKD) may elevate susceptibility to age-related macular degeneration (AMD) because of shared risk factors, pathogenic mechanisms, and genetic polymorphisms. Given the inconclusive findings in prior studies, we investigated this association using extensive datasets in the Asian Eye Epidemiology Consortium.

DESIGN

Cross-sectional study.

PARTICIPANTS

Fifty-one thousand two hundred fifty-three participants from 10 distinct population-based Asian studies.

METHODS

Age-related macular degeneration was defined using the Wisconsin Age-Related Maculopathy Grading System, the International Age-Related Maculopathy Epidemiological Study Group Classification, or the Beckman Clinical Classification. Chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 m2. A pooled analysis using individual-level participant data was performed to examine the associations between CKD and eGFR with AMD (early and late), adjusting for age, sex, hypertension, diabetes, body mass index, smoking status, total cholesterol, and study groups.

MAIN OUTCOME MEASURES

Odds ratio (OR) of early and late AMD.

RESULTS

Among 51 253 participants (mean age, 54.1 ± 14.5 years), 5079 had CKD (9.9%). The prevalence of early AMD was 9.0%, and that of late AMD was 0.71%. After adjusting for confounders, individuals with CKD were associated with higher odds of late AMD (OR, 1.46; 95% confidence interval [CI], 1.11-1.93; P = 0.008). Similarly, poorer kidney function (per 10-unit eGFR decrease) was associated with late AMD (OR, 1.12; 95% CI, 1.05-1.19; P = 0.001). Nevertheless, CKD and eGFR were not associated significantly with early AMD (all P ≥ 0.149).

CONCLUSIONS

Pooled analysis from 10 distinct Asian population-based studies revealed that CKD and compromised kidney function are associated significantly with late AMD. This finding further underscores the importance of ocular examinations in patients with CKD.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Utility of ChatGPT in Diabetic Retinopathy Risk Assessment: A Comparative Study with Clinical Diagnosis

Purpose

To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk.

Methods

This retrospective observational study utilized an anonymized dataset of 111 patients with diabetes who underwent a comprehensive eye examination along with clinical and biochemical assessments. Clinical and biochemical data along with and without central subfield thickness (CST) values of the macula from OCT were uploaded to ChatGPT-4, and the response from the ChatGPT was compared to the clinical DR diagnosis made by an ophthalmologist.

Results

The study assessed the consistency of responses provided by ChatGPT, yielding an Intraclass Correlation Coefficient (ICC) value of 0.936 (95% CI, 0.913-0.954, p < 0.001) (with CST) and 0.915 (95% CI, 0.706-0.846, p < 0.001) (without CST), both situations indicated excellent reliability. The sensitivity and specificity of ChatGPT in predicting the DR cases were evaluated. The results revealed a sensitivity of 67% with CST and 73% without CST. The specificity was 68% with CST and 54% without CST. However, Cohen's kappa revealed only a fair agreement between ChatGPT predictions and clinical DR status in both situations, with CST (kappa = 0.263, p = 0.005) and without CST (kappa = 0.351, p < 0.001).

Conclusion

This study suggests that ChatGPT has the potential of a preliminary DR screening tool with further optimization needed for clinical use.

Association of OCT and OCT angiography measures with the development and worsening of diabetic retinopathy in type 2 diabetes

OBJECTIVE

To assess if optical coherence tomography (OCT) and OCT angiography (OCTA) measures are associated with the development and worsening of diabetic retinopathy (DR) over four years.

METHODS

280 participants with type 2 diabetes underwent ultra-wide field fundus photography, OCT and OCTA. OCT-derived macular thickness measures, retinal nerve fibre layer and ganglion cell-inner plexiform layer thickness and OCTA-derived foveal avascular zone area, perimeter, circularity, vessel density (VD) and macular perfusion (MP) were examined in relation to the development and worsening of DR over four years.

RESULTS

After four years, 206 eyes of 219 participants were eligible for analysis. 27 of the 161 eyes (16.7%) with no DR at baseline developed new DR, which was associated with a higher baseline HbA1c and longer diabetes duration. Of the 45 eyes with non-proliferative DR (NPDR) at baseline, 17 (37.7%) showed DR progression. Baseline VD (12.90 vs. 14.90 mm/mm2, p = 0.032) and MP (31.79% vs. 36.96%, p = 0.043) were significantly lower in progressors compared to non-progressors. Progression of DR was inversely related to VD ((hazard ratio [HR] = 0.825) and to MP (HR = 0.936). The area under the receiver operating characteristic curves for VD was AUC = 0.643, with 77.4% sensitivity and 41.8% specificity for a cut-off of 15.85 mm/mm2 and for MP it was AUC = 0.635, with 77.4% sensitivity and 25.5% specificity for a cut-off of 40.8%.

CONCLUSIONS

OCTA metrics have utility in predicting progression rather than the development of DR in individuals with type 2 diabetes.

Burden of undiagnosed and suboptimally controlled diabetes in selected regions of India: Results from the SMART India population-level diabetes screening study

AIMS

To estimate the prevalence of undiagnosed diabetes and suboptimally controlled diabetes and the associated risk factors by community screening in India.

METHODS

In this multi-centre, cross-sectional study, house-to-house screening was conducted in people aged ≥40 years in urban and rural areas across 10 states and one union territory in India between November 2018 and March 2020. Participants underwent anthropometry, clinical and biochemical assessments. Capillary random blood glucose and point-of-care glycated haemoglobin (HbA1c ) were used to diagnose diabetes. The prevalence of undiagnosed diabetes and suboptimal control (HbA1c ≥53 mmol/mol [≥7%]) among those with known diabetes was assessed.

RESULTS

Among the 42,146 participants screened (22,150 urban, 19,996 rural), 5689 had known diabetes. The age-standardised prevalence of known diabetes was 13.1% (95% CI 12.8-13.4); 17.2% in urban areas and 9.4% in rural areas. The age-standardised prevalence of undiagnosed diabetes was 6.0% (95% CI 5.7-6.2); similar in both urban and rural areas with the highest proportions seen in the East (8.0%) and South (7.8%) regions. When we consider all people with diabetes in the population, 22.8% of individuals in urban areas and 36.7% in rural areas had undiagnosed diabetes. Almost 75% of the individuals with known diabetes had suboptimal glycaemic control.

CONCLUSIONS

High prevalence of undiagnosed diabetes and suboptimally controlled diabetes emphasises the urgent need to identify and optimally treat people with diabetes to reduce the burden of diabetes.

Developing a Risk Stratification Model Based on Machine Learning for Targeted Screening of Diabetic Retinopathy in the Indian Population

OBJECTIVE

This study aimed to develop a predictive risk score model based on deep learning (DL) independent of fundus photography, totally reliant on systemic data through targeted screening from a population-based study to diagnose diabetic retinopathy (DR) in the Indian population.

METHODS

It involved machine learning application on datasets of a cross-sectional population-based study. A total of 1425 subjects (1175 subjects with known diabetes and 250 with newly diagnosed diabetes) were included in the study. We applied five machine learning algorithms, random forest (RF), logistic regression (LR), support vector machines (SVM), artificial neural networks (ANN), and decision trees (DT), to predict diabetic retinopathy in our datasets. We incorporated a percentage split in the first experiment and randomly divided our data set into 80% as a training set and 20% as a test set. We performed a three-way data split in the second experiment to prevent overestimating predictive performance. We randomly divided our data set into 60% as a training set, 20% as a validation set, and 20% as the test set. Furthermore, we integrated five-fold cross-validation to split the percentage to evaluate our method. We judged the predictive performance based on the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy (Acc), sensitivity, and specificity.

RESULTS

The RF classifier achieved the best prediction performance with AUC, Acc, and sensitivity values of 0.91, 0.89, and 0.90, respectively, in the percentage split. Similarly, a three-way data split attained an outcome of 0.86 and 0.85 in AUC and Acc. Likewise, the five-fold cross-validation performed the best with results of 0.90, 0.97, 0.91, and 0.75 in AUC, Acc, sensitivity, and specificity, respectively.

CONCLUSION

Since the RF classifier achieved the best performance, we propose it to identify diabetic retinopathy for targeted screening in the general population.

Deep-Learning-Based Visualization and Volumetric Analysis of Fluid Regions in Optical Coherence Tomography Scans

Retinal volume computation is one of the critical steps in grading pathologies and evaluating the response to a treatment. We propose a deep-learning-based visualization tool to calculate the fluid volume in retinal optical coherence tomography (OCT) images. The pathologies under consideration are Intraretinal Fluid (IRF), Subretinal Fluid (SRF), and Pigmented Epithelial Detachment (PED). We develop a binary classification model for each of these pathologies using the Inception-ResNet-v2 and the small Inception-ResNet-v2 models. For visualization, we use several standard Class Activation Mapping (CAM) techniques, namely Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM, and Self-Matching CAM, to visualize the pathology-specific regions in the image and develop a novel Ensemble-CAM visualization technique for robust visualization of OCT images. In addition, we demonstrate a Graphical User Interface that takes the visualization heat maps as the input and calculates the fluid volume in the OCT C-scans. The volume is computed using both the region-growing algorithm and selective thresholding technique and compared with the ground-truth volume based on expert annotation. We compare the results obtained using the standard Inception-ResNet-v2 model with a small Inception-ResNet-v2 model, which has half the number of trainable parameters compared with the original model. This study shows the relevance and usefulness of deep-learning-based visualization techniques for reliable volumetric analysis.

Patient, family member, and health care provider perspective on barriers and facilitators to diabetic retinopathy screening in Thailand: A qualitative study

OBJECTIVES

Diabetic retinopathy (DR) can cause significant visual impairment which can be largely avoided by early detection through proper screening and treatment. People with DR face a number of challenges from early detection to treatment. The aim of this study was to investigate factors that influence DR screening in Thailand and to identify barriers to follow-up compliance from patient, family member, and health care provider (HCP) perspectives.

METHODS

A total of 15 focus group discussions (FGDs) were held, each with five to twelve participants. There were three distinct stakeholders: diabetic patients (n = 47) presenting to a diabetic retinopathy clinic in Thailand, their family members (n = 41), and health care providers (n = 34). All focus group conversations were transcribed verbatim. Thematic analysis was used to examine textual material.

RESULTS

Different themes emerged from the FGD on knowledge about diabetes, self-care behaviors of diabetes mellitus (DM), awareness about DR, barriers to DR screening, and the suggested solutions to address those barriers. Data showed lower knowledge and awareness about diabetes and DR in both patients and family members. Long waiting times, financial issues, and lack of a person to accompany appointments were identified as the major deterrents for attending DR screening. Family support for patients was found to vary widely, with some patients reporting to have received adequate support while others reported having received minimal support. Even though insurance covered the cost of attending diabetes/DR screening program, some patients did not show up for their appointments.

CONCLUSION

Patients need to be well-informed about the asymptomatic nature of diabetes and DR. Communication at the patient level and shared decision-making with HCPs are essential. Family members and non-physician clinicians (such as diabetes nurses, diabetes educators, physician assistants) who work in the field of diabetes play a vital role in encouraging patients to attend diabetes and DR follow-ups visits regularly.

Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma

Purpose

To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic.

Methods

This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed.

Results

Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55-92.45%], and retina grading had 91.90% (95% CI: 87.78-96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30-88.53%) and 99.45 (95% CI: 98.03-99.93), respectively; DR grading had 83.33% (95 CI: 51.59-97.91) and 98.86 (95% CI: 97.35-99.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively.

Conclusion

DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system.

Association of OCT biomarkers and visual impairment in patients with diabetic macular oedema with vitreomacular adhesion

BACKGROUND

To analyse the distribution of spectral domain optical coherence tomography (SD-OCT) biomarkers in different types of vitreomacular adhesion (VMA) associated visual impairment in diabetic macular oedema.

METHODS

A total of 317 eyes of 202 patients were enrolled. Cases were divided into two groups focal VMA and broad VMA and subjects with no VMA were enrolled as controls. A grading platform was used for evaluating the morphology of diabetic macular oedema (DME), using good-quality SD-OCT images. Grading was done for VMA and the biomarkers. Best corrected visual acuity (BCVA), central retinal thickness (CRT) and central subfield thickness (CSFT) was also recorded.

RESULTS

The CRT (p = <0.001) and CSFT (p = <0.001) values were statistically significant between the groups. Except for Inner Nuclear Layer Cysts (p = <0.001), absence of Bridging Tissue that is composed of muller cell fibers and bipolar cells (p<0.001), and Hyper Reflective Dots (HRD) in cyst (p = 0.006) there were no significant differences in the distribution of OCT biomarkers among the 3 groups (focal VMA, broad VMA and no VMA). Only Disorganization of Retinal Inner Layers (DRIL) (p = 0.044) showed significant association with vision impairment in all the 3 groups.

CONCLUSION

The distribution of OCT biomarkers was similar across all eyes of cases and controls. However, they were more likely to be associated with visual impairment in the presence of VMA than no VMA.

Colour vision restrictions for driving: an evidence-based perspective on regulations in ASEAN countries compared to other countries

Colour vision deficiency is an impairment in discriminating colours. Beyond occupational opportunities, colour vision-based restrictions may limit driving, which is a daily task for many people. This review aims to compare existing colour vision requirements for obtaining a driving license in southeast Asian countries to the rest of the world. Subsequently, to review existing published literature and provide evidence-based recommendations for future guidelines for colour-deficient drivers. Color vision requirements for obtaining a driving license vary widely amongst countries. While colour-deficient drivers may face mild challenges in driving, increased awareness and developing effective compensatory strategies could enable them to drive safely. The current evidence does not support a strict exclusion of all colour-deficient individuals from driving. Instead, emphasis is needed on screening to increase awareness and insight into their disability. Future studies should consider compensatory adaptive strategies that are specific for high-risk situations such as challenging driving conditions.

Distance and near vision changes after intravitreal anti-vascular endothelial growth factor injection in eyes with center-involving diabetic macular edema

Purpose

To elucidate distance and near vision changes after intravitreal injections in center-involving diabetic macular edema (CIDME) in phakic and pseudophakic groups.

Methods

A retrospective study was done on 148 eyes (72 phakic and 76 pseudophakic) with center-involving DME. All eyes were treated with intravitreal anti-vascular endothelial growth factor (VEGF) injection. All patients underwent distance best-corrected visual acuity (BCVA) testing, near BCVA testing, dilated fundus examination, and optical coherence tomography (OCT) at baseline and follow-up visits. Eyes that could not improve after the first injection were given 2nd, 3rd, and more injections in the subsequent visits.

Results

On follow-up, post injections in the phakic group (n = 72), there were 65 eyes (90.3%) with stable/improved near vision and 59 eyes (81.9%) with stable/improved distance vision, whereas in the pseudophakic group (n = 76), 63 eyes (82.9%) and 60 eyes (78.9%), respectively. Both in phakic and pseudophakic eyes, 7.7%-13% of the cohort showed only near vision improvement.

Conclusion

In DME, besides the changes in distance vision, there are also changes in near vision. These changes should be taken into account while determining the response to anti-VEGF in DME treatment.

Estimating the costs of blindness and moderate to severe visual impairment among people with diabetes in India

OBJECTIVES

This study provides an estimate of the annual cost of blindness and moderate to severe visual impairment (MSVI) among people with diabetes aged 40 years and above in India in the year 2019.

DESIGN

A cost of illness study.

SETTING

India.

PARTICIPANTS

People with diabetes aged 40 years and above in India in the year 2019.

PRIMARY AND SECONDARY OUTCOME MEASURES

Estimates are provided for the total costs of screening for most common vision-threatening eye conditions, treatment of these conditions, economic activity lost by these people and their family carers whose ability to work is affected, and loss of quality of life experienced by people with diabetes and blindness or MSVI.

RESULTS

It is estimated that for people with diabetes aged 40 years or above, annual screening followed by eye examination where required would cost around 42.3 billion Indian rupees (INR) (4230 crores) per year; treating sight problems around 2.87 billion INR (287 crores) per year if 20% of those needing treatment receive it; and lost economic activity around 472 billion INR (47 200 crores). Moreover, 2.86 million (0.286 crores) quality-adjusted life years (QALYs) are lost annually due to blindness and MSVI. The estimate of lost production is highly sensitive to the proportion of people with MSVI able to work and how their output compares with that of a person with no visual impairment.

CONCLUSIONS

This is the first study to estimate the cost of blindness and MSVI for people aged 40 years and over with diabetes in India. The annual cost to the Indian economy is substantial. This cost will be expected to fall if a successful screening and treatment plan is introduced in India. Further work is suggested using more robust data, when available, to estimate the loss of productivity and loss of QALYs, as this would be worthwhile.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors). The study uses a t-test and Shapely additive explanations (SHAP) to rank the risk factors. Then, it uses five machine learning models (K-Nearest Neighbor, Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes) to identify the unimportant risk factors based on the area under the curve criterion to predict DR. To determine the overall significance of risk variables, a weighted average of each classifier's importance is used. The ranking of risk variables is provided to machine learning models. To construct a model for DR prediction, the combination of risk factors with the highest AUC is chosen. The results show that the risk factors glycosylated hemoglobin and systolic blood pressure were present in the top three risk factors for DR in all five machine learning models when the t-test was used for ranking. Furthermore, the risk factors, namely, systolic blood pressure and history of hypertension, were present in the top five risk factors for DR in all the machine learning models when SHAP was used for ranking. Finally, when an ensemble of the five machine learning models was employed, independently with both the t-test and SHAP, systolic blood pressure and diabetes mellitus duration were present in the top four risk factors for diabetic retinopathy. Decision Tree and K-Nearest Neighbor resulted in the highest AUCs of 0.79 (t-test) and 0.77 (SHAP). Moreover, K-Nearest Neighbor predicted DR with 82.6% (t-test) and 78.3% (SHAP) accuracy.

'I don`t need an eye check-up'. A qualitative study using a behavioural model to understand treatment-seeking behaviour of patients with sight threatening diabetic retinopathy (STDR) in India

Diabetic Retinopathy (DR) affects about 27% of patients with diabetes globally. According to the World Health Organization (WHO), DR is responsible for37 million cases of blindness worldwide. The SMART India study (October 2020-August 2021) documented the prevalence of diabetes, and DR in people40 years and above across ten Indian states and one Union Territory by conducting community screening. About 90% of people with sight threatening diabetic retinopathy (STDR) were referred from this screening study to eye hospitals for management, but failed to attend. This qualitative study, a component of the SMART India study, explored perceptions of referred patients regarding their susceptibility to eye related problems in diabetes and the benefits/barriers to seeking care. Perceived barriers from the viewpoint of ophthalmologists were also explored. Guided by the Health Beliefs Model (HBM), 20 semi structured interviews were carried out with consenting patients diagnosed with STDR. They included nine patients who had sought care recruited from eight eye hospitals across different states in India and eleven patients who did not seek care. Eleven ophthalmologists also participated. Four themes of analysis based on the HBM were, understanding of DR and its treatment, perceptions about susceptibility and severity, perceived barriers, perceived benefits and cues to action. Findings revealed poor understanding of the effects of diabetes on the eye contributing to low risk perception. Prohibitive costs of treatment, difficulties in accessing care services and poor social support were major barriers to seeking care. Ophthalmologists acknowledged that the absence of symptoms and the slow progressive nature of the disease deluded patients into thinking that they were fine. The study attests to the need for greater health literacy around diabetes, DR and STDR; for making treatment more affordable and accessible and for the development of effective patient education and communication strategies towards increasing compliance.

Next-generation sequencing--based genetic testing and phenotype correlation in retinitis pigmentosa patients from India

Purpose

Inherited retinal dystrophies (IRD) are a heterogeneous group of retinal diseases leading to progressive loss of photoreceptors through apoptosis. Retinitis pigmentosa (RP) is considered the most common form of IRD. Panel-based testing in RP has proven effective in identifying the causative genetic mutations in 70% and 80% of the patients. This is a retrospective, observational, single-center study of 107 RP patients who had undergone next-generation sequencing-based targeted gene panel testing for IRD genes. These patients were inspected for common phenotypic features to arrive at meaningful genotype-phenotype correlation.

Methods

Patients underwent complete ophthalmic examination, and blood was collected from the proband for DNA extraction after documenting the pedigree. Targeted Next Generation Sequencing (NGS) was done by panel-based testing for IRD genes followed by co-segregation analysis wherever applicable.

Results

Of the 107 patients, 72 patients had pathogenic mutations. The mean age of onset of symptoms was 14 ± 12 years (range: 5-55). Mean (Best Corrected Visual Acuity) BCVA was 6/48 (0.9 logMAR) (range 0.0-3.0). At presentation, over one-third of eyes had BCVA worse than 6/60 (<1 logMAR). Phenotype analysis with the gene defects showed overlapping features, such as peripheral well-defined chorioretinal atrophic patches in patients with CERKL, PROM1, and RPE65 gene mutations and large macular lesions in patients with RDH12 and CRX gene mutations, respectively. Nummular or clump-like pigmentation was noted in CRB1, TTC8, PDE6A, and PDE6B.

Conclusion

NGS-based genetic testing can help clinicians to diagnose RP more accurately, and phenotypic correlations can also help in better patient counselling with respect to prognosis and guidance regarding ongoing newer gene-based therapies.

Compliance with follow-up in patients with diabetic macular edema: Eye care center vs. diabetes care center

Purpose

The study was conducted to compare the compliance to intravitreal injection treatment and follow-up in patients with center-involving diabetic macular edema (CI-DME) and treatment outcomes between a tertiary eye care facility and a tertiary diabetes care center.

Methods

A retrospective review was conducted on treatment naïve DME patients who had received intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections in 2019. Participants were people with type 2 diabetes who were under regular care at the eye care center or the diabetes care center in Chennai. The outcome measures were noted at months 1, 2, 3, 6, and 12.

Results

A review of 136 patients treated for CI-DME (72 from the eye care center and 64 from a diabetes care center) was carried out. The severity of diabetic retinopathy (DR) was similar in both centers. There was no statistically significant (P > 0.05) difference in the choice of initial intravitreal drug in the two centers. At 12-month follow-up, only 29.16% came for a follow-up in the eye center vs. 76.56% in a diabetes care center (P = 0.000). The multivariate logistic regression showed increasing age was associated with non-compliance in both the groups (eye care center: odds ratio [OR] 0.91; 95% confidence interval [CI] 0.82-1.21; P = 0.044) and diabetes care center (OR 1.15; 95% CI 1.02-1.29; P = 0.020).

Conclusion

The follow-up rate between eye care and diabetic care center with DME showed a significant disparity. By providing comprehensive diabetes care for all complications under one roof, compliance with follow-up can be improved in people with DME.

Preliminary Report on Optical Coherence Tomography Angiography Biomarkers in Non-Responders and Responders to Intravitreal Anti-VEGF Injection for Diabetic Macular Oedema

PURPOSE

To identify optical coherence tomography angiography (OCTA) biomarkers in patients who were treated for diabetic macular oedema (DME) with intravitreal anti-vascular endothelial growth factor (VEGF) injections and compare the OCTA parameters between responders and non-responders.

METHODS

A retrospective cohort study of 61 eyes with DME who received at least one intravitreal anti-VEGF injection was included between July 2017 and October 2020. The subjects underwent a comprehensive eye examination followed by an OCTA examination before and after intravitreal anti-VEGF injection. Demographic data, visual acuity, and OCTA parameters were documented, and further analysis was performed pre- and post-intravitreal anti-VEGF injection.

RESULTS

Out of 61 eyes which underwent intravitreal anti-VEGF injection for diabetic macular oedema, 30 were responders (group 1) and 31 were non-responders (group 2). We found that the responders (group 1) had a higher vessel density in the outer ring that was statistically significant (p = 0.022), and higher perfusion density was noted in the outer ring (p = 0.012) and full ring (p = 0.044) at levels of the superficial capillary plexus (SCP). We also observed a lower vessel diameter index in the deep capillary plexus (DCP) in responders when compared to non-responders (p < 0.00).

CONCLUSION

The evaluation of SCP in OCTA in addition to DCP can result in a better prediction of treatment response and early management in diabetic macular oedema.

Single-bolus dexmedetomidine in prevention of emergence delirium in pediatric ophthalmic surgeries: A randomized controlled trial

Purpose

Emergency delirium (ED), a common postoperative neurologic complication, causes behavioral disturbances leading to self-traumas and also has long-term adverse effects in children. Our aim was to investigate the efficacy of a single-bolus dose of dexmedetomidine in reducing the incidence of ED. Additionally, pain relief, number of patients who needed rescue analgesia, hemodynamic parameters, and adverse events were assessed.

Methods

One hundred and one patients were randomly allocated into two groups: 50 patients received 15 mL of dexmedetomidine 0.4 μg/kg (group D) and 51 patients received volume-matched normal saline (group C). Hemodynamic parameters such as heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were monitored regularly throughout the procedure. ED was assessed with Pediatric Anesthesia Emergence Delirium Scale (PAEDS), and pain was measured using the modified Objective Pain Score (MOPS).

Results

The incidences of ED and pain were higher in group C than group D (P < 0.0001 and P < 0.0001, respectively). Group D showed significant decrease in MOPS and PAEDS values at 5, 10, 15, and 20 min (P < 0.05), HR at 5 min (P < 0.0243), and SBP at 15 min (P < 0.0127). There was no significant difference in DBP between the two groups at any time point. The mean blood pressure (MBP) at 10 min was significantly less in group D than group C (P < 0.001).

Conclusion

Dexmedetomidine 0.4 μg/kg as a single bolus over 10 min immediately after intubation is effective for the prevention of ED and significantly reduces the need of rescue analgesia without compromising the hemodynamic parameters in children undergoing ophthalmic surgery.

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96).

The contribution of dynamics to macaque body and face patch responses

Previous functional imaging studies demonstrated body-selective patches in the primate visual temporal cortex, comparing activations to static bodies and static images of other categories. However, the use of static instead of dynamic displays of moving bodies may have underestimated the extent of the body patch network. Indeed, body dynamics provide information about action and emotion and may be processed in patches not activated by static images. Thus, to map with fMRI the full extent of the macaque body patch system in the visual temporal cortex, we employed dynamic displays of natural-acting monkey bodies, dynamic monkey faces, objects, and scrambled versions of these videos, all presented during fixation. We found nine body patches in the visual temporal cortex, starting posteriorly in the superior temporal sulcus (STS) and ending anteriorly in the temporal pole. Unlike for static images, body patches were present consistently in both the lower and upper banks of the STS. Overall, body patches showed a higher activation by dynamic displays than by matched static images, which, for identical stimulus displays, was less the case for the neighboring face patches. These data provide the groundwork for future single-unit recording studies to reveal the spatiotemporal features the neurons of these body patches encode. These fMRI findings suggest that dynamics have a stronger contribution to population responses in body than face patches.

Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program

INTRODUCTION

Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption.

METHODS

In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters.

RESULTS

From a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance.

CONCLUSION

DR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.

Role of eccentric viewing training with microperimeter for low vision rehabilitation in multifocal choroiditis: Case report

A 21-year-old student with healed multifocal choroiditis involving the macula presented with difficulty in seeing distance and reading her college books. Based on microperimetr'y, the location of her preferred retinal locus and the position of new trained retinal locus (TRL) were identified. With multiple sessions of biofeedback training, her mean retinal sensitivity improved from 4.8 to 8.8 dl, distance vision from 3/60 to 6/36, and near vision from N32 to N8 in the right eye. At 6-month follow-up, her TRL position and visual acuity were found to be stable. This case report highlights that eccentric viewing training using microperimeter can significantly improve the efficiency of functional vision in patients with choroiditis.

Changes in visual acuity using low vision devices in patients with Leber hereditary optic neuropathy: A retrospective study

Purpose

This study was conducted to estimate the visual acuity improvement in patients with Leber hereditary optic neuropathy (LHON) with the help of low vision devices (LVDs) and to analyze the types of distant and near LVDs prescribed to the patients with LHON.

Methods

A retrospective case review of 74 subjects with LHON who were referred to a low vision care clinic at a tertiary eye center from 2016 to 2019 were recruited. The reason for referral was assessed from the patients' electronic medical records (EMR). Demographic data of the patients, visual acuity status, type of LVD prescribed, and visual acuity improvement with LVD were documented.

Results

Out of 74 patients, 91.9% (n = 68) were male, and the median age of patients was 21 (16) years. A 4× monocular telescope was prescribed for 2.7% of patients (n = 2) and SEETV binocular telescope for 1.4% (n = 1) was advised for distance. The most commonly prescribed near LVD was the 6× cutaway stand magnifier for 22 patients (29.7%). Four patients (5.4%) were prescribed with Notex, the most commonly prescribed non-optical LVD. Niki CCTV (12.2%, n=9) was the most commonly prescribed assistive device. The subjects were divided into three groups based on age: group I consisted of those <18 years of age, group II 18-40 years, and group III >40 years for the interpretation of visual improvement. There was a statistically significant improvement (group I: P < 0.001, group II: P < 0.0001, group III: P < 0.003) in near vision with help of LVDs in all three groups.

Conclusion

The use of LVDs and rehabilitation can help patients with LHON to lead a better life and will be more beneficial.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.

Intravitreal Anti-Vascular Endothelial Growth Factor and Moxifloxacin for Treatment of Tubercular Choroidal Granuloma and Sub retinal Abscess

PURPOSE

To report the treatment response of a case of tubercular choroidal granuloma and subretinal abscess with intravitreal anti-vascular endothelial growth factor (anti-VEGF) and moxifloxacin.

METHOD

Retrospective interventional case report.

RESULTS

A 34-year-old man presented with bilateral choroidal granuloma and subretinal abscess in one eye. He was a known case of spinal tuberculosis and was on anti-tubercular therapy. Patient was successfully treated with intravitreal injection of anti-VEGF and moxifloxacin.

CONCLUSION

Intravitreal anti-VEGF and moxifloxacin combination may be beneficial in tubercular choroidal granuloma and subretinal abscess.

Anatomical and Functional Outcomes in Delayed Onset versus Concurrent Retinal Detachment in Endophthalmitis

Purpose

To determine the functional and anatomical outcomes of patients with endophthalmitis with concurrent or delayed onset retinal detachment (RD), and compare the preoperative, intraoperative and postoperative features.

Patients and Methods

This was a retrospective review of 121 eyes in 121 patients presenting with endophthalmitis and RD. Subjects were categorized into two groups: endophthalmitis with delayed onset RD (group 1, N=76) and endophthalmitis with concurrent RD (group 2, N=45).

Results

The mean age of patients in groups 1 and 2 was 38.21±21.60 and 46.78±24.42 years, respectively (P=0.047). Exogenous endophthalmitis was common in both groups 1 and 2 (86.84% and 84.44%, respectively). No significant differences were found between the groups in the type of RD, retinal breaks, number of quadrants involved or proliferative vitreoretinopathy grade. In the overall cohort, visual acuity improved post-surgery in one-third of the patients who were in the near or total blindness category at presentation. We found good anatomical success rates of an attached retina in both groups 1 and 2 (84.3% and 77.7%, P=0.376).

Conclusion

Our study presents the results of patients with endophthalmitis and delayed onset RD or concurrent RD. It shows a few differences in presentation between the groups, but the anatomical and functional outcomes were almost the same.

Low-vision intervention for oculocutaneous albinism in a Tertiary Eye Care Hospital in India

PURPOSE

The objective of this study on patients with albinism in different age groups was to compare their level of visual impairment with the low-vision intervention (LVI) and its benefit.

METHODS

The medical records of 72 patients with low vision secondary to albinism who were referred to the low vision care clinic from 2015 to 2017 were analyzed. This included the demographic profile such as age, gender, occupation, ocular history, visual acuity status, and type of low-vision device (LVD) preferred. The LVDs prescribed and its subsequent improvement was compared.

RESULTS

In this data, 70 (97.2%) people had oculocutaneous albinism and 2 (2.8%) had ocular albinism. Majority of the patients had hyperopic astigmatism 42 (58.3%) and with-the-rule astigmatism 58 (93.5%). Presenting mean visual acuity for distance was noted to be 0.88 logMAR which improved to 0.83 logMAR with the help of spectacle correction. The most commonly prescribed LVD was a dome magnifier for 15 (34.9%) patients. In all the patients, there was statistically significant improvement (P < 0.05) in near vision with the help of LVDs.

CONCLUSION

The study highlights the importance of appropriate LVI for each subdivided age group. Patients with albinism who have received medical and surgical treatment have no or a limited role in restoring useful vision.

An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization

Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.

Prescribing patterns of low vision devices in patients with cone-related dystrophies

Purpose

The aim of this study was to elucidate the type of low vision devices (LVDs) prescribed for patients with cone dystrophy, cone-rod dystrophy, and rod-cone dystrophy and to analyze the visual improvement with the devices.

Methods

A retrospective review of 300 electronic medical records of patients with cone dystrophy, cone-rod dystrophy, and rod-cone dystrophy referred to the low vision care (LVC) clinic for the first time between 2014 and 2016 at a tertiary eye care center was done. Collected data included the demographic profile of patients, details of LVDs, and best-corrected vision.

Results

Out of 300 patients, 62.6% (n = 188) were male and 37.3% (n = 112) were female. Of the cases, 50% (n = 150) had cone-rod dystrophy, 45% (n = 135) had cone dystrophy, and 5% (n = 15) had rod-cone dystrophy. The most commonly prescribed LVD was SEE-TV binocular telescope (n = 6, 2.0%) for distance and dome magnifier (n = 60, 20%) for near. ET-40 dark grey tint (20.6%) was preferred for managing photophobia. There was a statistically significant difference in both distance and near visual acuities with LVDs (P < 0.05) in all categories, except rod-cone dystrophy.

Conclusion

Early diagnosis with appropriate prescription of LVDs including tints helps in achieving good quality of vision in patients with cone-related dystrophies.

Recommended protocol and technique for doing oral fundus fluorescein angiography for adults and children

Fundus fluorescein angiography (FFA) is usually performed intravenously through injection of sodium fluorescein dye. This procedure is difficult to perform in children and patients who are afraid of intravenous needles. Oral FFA can serve as a useful alternative to intravenous FFA in many cases and gives reliable results. We describe the recommended protocol and technique for doing oral FFA in adults and children.