Aravind Eye Hospitals is a hospital chain in India. It was founded by Dr. Govindappa Venkataswamy (popularly known as Dr.V) at Madurai, Tamil Nadu in 1976. It has grown into a network of eye hospitals and has had a major impact in eradicating cataract related blindness in India. As of 2012, Aravind has treated nearly 32 million patients and performed 4 million surgeries. The model of Aravind Eye Care hospitals has been applauded and has become a subject for numerous case studies across the world.
This Research Letter discusses steroid treatment and the Steroids and Cross-Linking Ulcer Treatment Trial II (SCUT II).
Objectives: Our primary objective was to identify the rate of antifungal resistance (including multidrug resistance) of fungal isolates cultured from fungal keratitis patients in South India. Our secondary objective was to identify associations between antifungal resistance and patient outcome. Subjects/methods: 153 patients attending the cornea clinic at Aravind Eye Hospital, Madurai (May-August 2025) with culture-positive fungal keratitis were enrolled in the study. Cultured isolates were evaluated for natamycin, amphotericin B, voriconazole and econazole minimum inhibitory concentration (MIC; n = 151-153). MIC data were analysed by Kruskal-Wallis test followed by Dunn's multiple comparisons. Patient disease outcome relative to isolate resistance was analysed by Fisher's exact test (n = 91). Results: There were high levels of resistance to all four antifungals, which were significantly elevated in Fusarium spp. and Aspergillus spp. isolates compared to other fungal genera (P < 0.0001). Fusarium spp. isolates (n = 81) resistant to: natamycin: 38.3%; amphotericin B: 93.8%; voriconazole: 97.5%; and econazole: 76.5%. Aspergillus spp. isolates (n = 33) resistant to: natamycin: 66.7%; amphotericin B: 87.9%; voriconazole: 6.1%; and econazole: 0%. Overall, fungal resistance to natamycin significantly correlated with an adverse clinical outcome, P = 0.0034, relative risk 1.7 (95% CI: 1.2-2.6). Conclusions: The majority of fungal isolates were resistant to multiple antifungals; none of the Fusarium isolates were susceptible to all four drugs, and 28% were resistant to all of them. Resistance to natamycin may worsen clinical outcome, particularly for infections caused by Aspergillus, even when susceptible to azoles. Our data support the need for species-specific MIC thresholds to have increased clinical utility.
PURPOSE:To compare the epidemiology and phenotypic characteristics of 2 populations of patients with retinopathy of prematurity (ROP) using a quantitative artificial intelligence (AI)-derived vascular severity score (VSS). METHODS:We retrospectively analyzed 2 data sets of ROP images: (1) The iROP data set including 1019 babies and 6552 eye-level examinations developed as part of a large US multicenter cohort study from 2012 to 2020. (2) 8281 consecutive ROP telemedicine examinations from 2363 babies in the Indian Aravind Eye Care System (AECS), from 2019 to 2020. We analyzed all eye examinations using the iROP DL system, which outputs a VSS from 1 to 9. The main outcome was the relationship between the VSS and the zone, stage, and extent of stage 3 in each data set, assessed using generalized estimating equations to account for the correlation from bilateral and repeated examinations. RESULTS:In both data sets, higher VSS was associated with higher stage (P < .001 for all stages in both data sets), higher extent of stage 3 (i-ROP, P =.004, and AECS, P < .001), and a more posterior zone for a given stage of ROP (i-ROP: stage 1, P = .04, stage 2, P < .001, and stage 3, P < .001; AECS: stage 1, P < .001, and stage 3, P < .001). For any given stage, the VSS was lower in the AECS data set than in the i-ROP data set (all P < .001). CONCLUSIONS:In both populations, the VSS was associated with more posterior zone and higher ROP stage/extent, though on average the VSS was lower in the AECS population. A correlation between measured VSS and intraocular vascular endothelial growth factor concentration might explain both findings.
Microbial keratitis requires rapid pathogen identification to guide treatment, but culture- and PCR-based diagnostics are slow and resource-intensive. We developed a triple-phase multimodal framework for bacterial-versus-fungal keratitis classification using slit-lamp photographs acquired under blue-light, sclerotic-scatter, and white-light illumination, together with clinical metadata. The model combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. We evaluated the framework on a multicenter dataset of 1,645 patients and 17,158 images from India and the United States. The model achieved 85.84% accuracy, 84.46% average F1-score, and 0.885 AUC. Site-specific evaluation showed that pooled results were overly optimistic, whereas resampling- and balance-based re-evaluation provided a more realistic assessment of cross-site generalization. Under all settings, our framework remained the top-performing approach. The code is available at https://github.com/yqwang01/TPMKA and dataset access will be provided subject to University of Michigan data-sharing clearance.
