Revolutionizing glaucoma care: harnessing artificial intelligence for precise diagnosis and management

particularly machine learning (ML) and deep learning (DL) algorithms, have shown potential to address these challenges by enhancing diagnostic precision, predicting disease progression, and automating clinical workflows. 6Convolutional neural networks (CNNs), for example, have demonstrated high sensitivity and specificity in analyzing imaging data such as fundus photographs and OCT scans. 7Recent studies have further validated AI's impact in glaucoma diagnostics.Chuter et al. 8 reported that AIassisted OCT analysis improved early glaucoma diagnosis rates by 30% compared to conventional methods.Similarly, Gong et al. 9 highlighted that AI-powered screening tools enhanced sensitivity in detecting glaucomatous changes by 25%, particularly in early-stage disease cases.