Sankara Nethralaya is a not-for-profit missionary institution for ophthalmic care (i.e., an eye hospital) headquartered in Chennai, India. In the name "Sankara Nethralaya", "Sankara" is a reference to Adi Shankaracharya and "Nethralaya" means "The Temple of the Eye". Sankara Nethralaya receives patients from India and abroad. Sankara Nethralaya has over 1000 employees and serves around 1500 patients per day, performing over 100 surgeries per day. The annual revenue as per the taxes is close to US$100 million.Sankara Nethralaya was rated among the top four ophthalmic institutions worldwide in 2020 by Newsweek. Nani A. Palkivala, former Indian ambassador to United States, described Sankara Nethralaya as the "Best managed charitable organization in India".
To compare automated and manual quantification of retinal haemorrhages in eyes with diabetic retinopathy (DR) and to analyse the risk of progression to proliferative DR (PDR). Retinal haemorrhages on ultra-widefield (UWF) pseudocolor images in eyes with non-proliferative diabetic retinopathy (NPDR) were manually segmented. DR severity was assessed within the seven ETDRS fields at baseline and 1-year follow-up. Lesions were also automatically segmented using EyeRead UWF software (Eyenuk) and the frequency and area of retinal haemorrhages and the average distance of haemorrhages from the optic nerve centre were computed. Manual and automated results were compared and correlated with progression to PDR at one year. Sixty-three eyes with NPDR at baseline were included, of which 29 progressed to PDR over one year. The automated measurements of total haemorrhage frequency, area and the distance from the optic nerve were significantly lower compared to manual grading, but the parameters were significantly correlated (r = 0.5–0.96; all P < 0.001). The distance of the haemorrhages from the optic nerve was found to be a significant risk factor for progression to proliferative DR from both approaches. Automated detection of retinal haemorrhages may serve as a surrogate to the manual grading in predicting the progression to PDR. Although the automated approach detects a lower number of lesions compared to manual grading, the results are correlated and the automatically determined parameters are still predictive of progression.
To describe the clinical and multimodal imaging characteristics of congenital grouped albinotic spots (CGAS) and to explore their functional implications using full-field electroretinography (ERG). Two patients with incidentally detected CGAS underwent comprehensive ophthalmic examination, including best-corrected visual acuity, refraction, dilated fundus evaluation, swept-source optical coherence tomography (SSOCT), fundus autofluorescence (FAF) imaging, and full-field ERG. Longitudinal follow-up was available for one case over 7 years. Clinical and imaging findings were qualitatively compared with previously reported CGAS features and with differential diagnoses of flecked retina disorders. Both patients demonstrated numerous, bilaterally symmetrical, flat hypopigmented lesions that were small and round at the posterior pole, sparing the fovea and larger, linear, and radially oriented in the periphery. FAF showed striking hyperautofluorescence corresponding to the hypopigmented patches. SSOCT in both cases showed preserved foveal contour, normal retinal architecture, and intact RPE–photoreceptor complex. Visual acuity was 20/20 in both patients at last examination, after refractive correction. ERG responses were largely within normal reference limits, although each case exhibited minimally reduced photopic responses. CGAS represents a benign, nonprogressive RPE abnormality with characteristic peripheral hypopigmented spots that are paradoxically hyperautofluorescent on FAF and associated with preserved retinal structure and good visual function. FAF and OCT are valuable, non-invasive tools for confirming the diagnosis, delineating lesion extent, and distinguishing CGAS from inherited flecked-retina dystrophies. Additional case series and genetic studies are warranted to clarify underlying mechanisms and potential associations.
Anti-vascular endothelial growth factor (anti-VEGF) therapy is the mainstay of management for diabetic macular edema (DME), but marked variability in response, high injection frequency, and cumulative treatment burden highlight the need for tools that can individualize treatment beyond protocol-driven regimens. Artificial intelligence (AI) offers a pathway toward more individualized risk stratification and prognostic support primarily by capturing statistical associations rather than biological mechanisms. Deep learning systems have achieved great accuracy in detecting diabetic retinopathy (DR) and DME. Several autonomous DR/DME screening solutions are in clinical use. Recent advances have applied supervised machine learning, convolutional neural networks, generative adversarial networks, and ensemble methods to multimodal data from fundus images, baseline and follow-up optical coherence tomography (OCT), along with clinical and biochemical data, to classify likely responders and non-responders. These models automatically quantify and track imaging biomarkers to accurately predict central subfield thickness and vision outcomes after loading doses, and estimate future injection burden. AI-driven decision-support tools analyze vast amounts of patient data, treatment histories, and integrate multimodal data, including fundus images, OCT images, and systemic data to provide recommendations for optimal treatment and follow-up, tailored to each individual profile. The AI systems can potentially generate individualized risk and response profiles that can support decisions on initiating therapy, choosing between agents, tailoring treat-and-extend intervals, and timing switches to steroids or combination strategies. However, issues of generalizability, transparency, workflow integration, and ethical deployment need to be systematically addressed. AI-enabled decision support for patient selection and treatment response prediction is poised to become an integral component of anti-VEGF therapy.
PURPOSE:This study analyzes the efficacy and safety of combination of methotrexate and mycophenolate mofetil with treatment-resistant uveitis and scleritis. METHOD:Retrospective chart review. RESULT:The study included five patients with uveitis and one patient with scleritis who received combination of antimetabolites. The median age of these patients was 19.5 years, and all of them had bilateral involvement. Two patients had juvenile idiopathic arthritis while one patient each had Behçet disease, Eales disease, undifferentiated anterior scleritis, and pars planitis. The median duration of immunosuppressive and/or corticosteroid treatment before switching to combined therapy was 217 (192-1,325) days. Remission of uveitis was achieved in all patients. The median duration of follow-up in these patients was 338.5 (209-901) days. At the end of the follow-up, topical steroids were withdrawn in three eyes, and oral corticosteroids were tapered off completely. All except one patient tolerated the combined treatment. CONCLUSION:The combination of oral methotrexate and mycophenolate mofetil can be a useful alternative in patients resistant to conventional immunosuppression.
