Aravind Eye Hospital

This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proactive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management.

PURPOSE:To develop and validate an artificial intelligence (AI)-enabled eye rubbing detection tool using sensor data collected from wrist-based wearable devices. METHODS:An automated system was designed to detect eye rubbing using wrist-based wearable devices. The system involves 3 components: sensor data acquisition, data preprocessing, and deep learning-based classification model. Six different deep learning architectures were developed, including 1D and 2D CNN-LSTM models and an ensemble, to identify the most effective approach. Two datasets were established in the time and frequency domains: a timeseries dataset contains 8640 recordings and a scalogram dataset 15 comprising 112,320 images from 20 subjects. RESULTS:The proposed system demonstrated strong performance, achieving an F1-score of 95.27 ± 0.87% and AUC of 98.26 ± 0.92% across 5 cross-validation folds when using the 1D CNN-LSTM model to distinguish eye rubbing and noneye rubbing activities. When evaluated on the testing set, the system maintained high performance, with an F1-score of 92.54% and AUC of 96.70%. Model inference required 15.32 milliseconds per segment, supporting real-time operation and practical deployments. CONCLUSIONS:The proposed system provides high reliability in detecting eye rubbing behaviors, indicating its potential as a tool to support ophthalmologists and researchers in the rigorous study of the contributions of eye rubbing to the development and progression of keratoconus and other corneal ectasias.

Surgical care for the disease is required in a significant number of people worldwide. In India, approximately 4% of the population needs surgical care. In ophthalmology, lens-based surgeries account for a significant contribution to the list. In this massive volume of surgeries, a proper sterilization process is vital for the successful outcome of these surgeries. Healthcare professionals must have an in-depth understanding of the sterilization process, which helps reduce complications and prevent terminal events.

Purpose: To evaluate the impact of blue-filtering intraocular lenses (BFIOLs) on retinal oxidative stress by comparing 8-hydroxy-2’-deoxyguanosine (8-OHdG) levels in the macular retina and submacular retinal pigment epithelium (RPE) of donor eyes with BFIOLs and non-BFIOLs. Design: Cross-sectional laboratory study using postmortem human donor eyes. Methods: Fifty-eight eyes from 39 donors were categorized as BFIOL (n = 16), non-BFIOL (n = 24), or phakic (n = 18). Macular retina and submacular RPE were dissected, genomic DNA extracted, and 8-OHdG quantified using ELISA. 8-OHdG levels were compared across lens groups, and associations with age and sex were examined. Results: Submacular RPE had higher 8-OHdG levels than the macular retina (0.95 vs 0.44 ng/mL; P < 0.0001). No significant differences in 8-OHdG were observed between BFIOL and non-BFIOL eyes in either tissue (all P ≥ 0.09). Age was not associated with 8-OHdG, whereas male donors showed higher RPE 8-OHdG levels than female donors (P = 0.03). Conclusions: Macular retinal and RPE 8-OHdG levels did not differ significantly by IOL type. These biochemical observations, considered alongside existing clinical data, suggest that any retinal protective effect of BFIOLs on oxidative DNA damage is likely to be modest.