Clinical characteristics of primary glaucoma and diagnostic performance of the cybersight artificial intelligence model
Original article | Vol. 18 No. 6 (2026)
Journal of Clinical Medicine Hue Central Hospital, Vol. 18 No. 6 (2026)
Original article

Clinical characteristics of primary glaucoma and diagnostic performance of the cybersight artificial intelligence model

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Uyen, P. N., Thanh, D. K., Phuc, H. L., Khanh, N. L. N., & Van, P. T. (2026). Clinical characteristics of primary glaucoma and diagnostic performance of the cybersight artificial intelligence model . Journal of Clinical Medicine Hue Central Hospital, 18(6). https://doi.org/10.38103/jcmhch.18.6.16
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DOI: 10.38103/jcmhch.18.6.16
10.38103/jcmhch.18.6.16
Phan Nha Uyen
Doan Kim Thanh
Pham Ngoc Thach University of Medicine, Ho Chi Minh City
Hoang Le Phuc
Children's Hospital 1, Ho Chi Minh City
Nguyen Le Ngoc Khanh
Pham Trong Van

Abstract

Objective: To describe clinical, structural and functional characteristics of primary glaucoma; assess the relationship between disease severity and Cybersight AI outputs; and determine diagnostic performance at the system-provided 20% threshold at baseline and 12-month follow-up.

Methods: This prospective single-center longitudinal observational study with a diagnostic-accuracy component was conducted from December 2024 to August 2026 at Eye Center, Hue Central Hospital. Baseline analysis included 229 eyes from 121 patients; paired 12-month analysis included 192 eyes from 100 patients. The reference standard integrated specialist examination, optic-disc assessment, RNFL optical coherence tomography and Humphrey 24-2 visual fields. Generalized estimating equations and 5,000 patient-cluster bootstrap replicates accounted for inter-eye correlation.

Results: Primary glaucoma was diagnosed in 133/229 eyes (58.1%). At the 20% threshold, sensitivity was 51.9% (95% CI 41.9-61.8), specificity 90.6% (83.8-96.4), PPV 88.5%, NPV 57.6%, accuracy 68.1% and AUC 0.744. AI positivity increased with glaucoma severity. Among paired eyes, sensitivity was 57.8% and specificity 80.7%; AUC did not change significantly.

Conclusion: At the 20% threshold, Cybersight AI showed high specificity but limited sensitivity. Detection was more frequent in eyes with greater structural and functional damage. A negative AI result was insufficient to exclude glaucoma, and the system should not replace specialist assessment.

Keywords:  primary glaucoma, artificial intelligence, Cybersight AI, fundus photography, optical coherence tomography, retinal nerve fiber layer, 24-2 visual field
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