Sample Characteristics: Glaucoma Detection Accuracy for Two AI Systems

370 eyes from 193 patients were used to evaluate the diagnostic accuracy of two AI systems for glaucoma detection from color fundus photographs. The dataset, created by Gilvan Vilarinho da Silva Filho, includes results for the Laguna ONhE and VUNO Med-Fundus AI systems, showing AUCs of 0.879 and 0.857 respectively. It was last updated on 2026-05-28.

Use Cases

• Benchmarking AI diagnostic performance based on reported AUC metrics.
• Comparing combined AI system accuracy to traditional clinical measures like Global Mean Deviation.
• Assessing the potential for CFP-based screening in resource-limited settings as described in the conclusions.

Strengths

• Includes a specific sample size of 370 eyes from 193 patients.
• Reports concrete diagnostic accuracy metrics (AUC) for two commercial AI systems.
• Licensed under CC-BY-4.0 for open reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

Gilvan Vilarinho da Silva Filho

Collection Method

Retrospective cross-sectional study using swept-source OCT and visual field testing.

Freshness

Last updated 2026-05-28 17:34:32; freshness should be verified.