Glaucoma diagnostics and imaging technology represent a rapidly evolving area at the intersection of clinical ophthalmology, biomedical engineering, and data science. Glaucoma remains a leading cause of irreversible blindness worldwide, with early detection and monitoring posing significant clinical challenges. Modern glaucoma diagnostics combine structural and functional evaluation to facilitate early detection, risk assessment, and progression monitoring. The field is witnessing a shift from traditional methods—such as tonometry, fundus examination, and standard automated perimetry—toward more sophisticated tools, including optical coherence tomography (OCT), OCT-angiography, home-based intraocular pressure (IOP) monitoring, and artificial intelligence (AI)-driven technologies. These advances promise better risk prediction, earlier diagnosis, and greater precision in monitoring disease progression.

However, important limitations remain. The structure–function relationship in glaucoma is non-linear: early damage may be asymptomatic and underestimated by perimetry, while advanced disease can show floor effects on imaging, limiting its utility. There is still no universally accepted gold standard for defining early glaucoma, which complicates the validation of new technologies and large-scale screening strategies. Perimetry suffers from high test–retest variability, learning effects, and differences between manufacturers’ algorithms. OCT and other imaging modalities are affected by artefacts, segmentation and displacement errors, anatomical variability, and reference-database bias, and may produce discordant results compared with visual fields. AI and deep learning systems are being tested for scalable, automated analysis of fundus and OCT images, but face challenges related to inconsistent raw data, a lack of longitudinal measurements, and limited generalizability across devices and populations. Wider implementation of improved procedures and innovations is further constrained by cost, access to advanced imaging, and the absence of standardized reporting across manufacturers.

This collection invites original research, reviews, case series, and perspectives on emerging and established technologies for glaucoma diagnosis and monitoring, with an emphasis on translational and clinical relevance. Topics of interest include, but are not limited to:

- Optical coherence tomography (all generations and segmentation strategies)
- OCT angiography and related vascular imaging approaches
- Perimetry studies and innovations, including strategies to reduce variability
- Tele-ophthalmology workflows for glaucoma screening and follow-up
- Multimodal structure–function integration and decision-support tools
- AI and machine learning methods for image analysis, risk stratification, progression prediction, and clinical decision support
- Strategies to address test performance limitations, artefacts, and cross-device reproducibility
- Implementation, accessibility, and standardization challenges in glaucoma diagnostics.
- IOP monitoring devices, particularly but not limited to home- and continuous-IOP monitoring devices.

Conflict of interest declaration:
Topic Editor Geeta Behera has received a grant from Appasamy Associates to conduct a sponsored trial.
Topic editor Deepta Ghate has intellectual property rights shared with EON Realty for a pupil simulator and has been a consultant for FAmygen phamaceuticals.

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Emerging Technologies and Translational Advances in Glaucoma Diagnostics