Evaluating the Influence of Clinical Data on Inter-Observer Variability in Optic Disc Analysis for AI-Assisted Glaucoma Screening.

Purpose: This study aims to evaluate the inter-observer variability in assessing the optic disc in fundus photographs and its implications for establishing ground truth in AI research.

Methods: Seventy subjects were screened during a screening campaign. Fundus photographs were classified into normal (NL) or abnormal (GS: glaucoma and glaucoma suspects) by two masked glaucoma specialists. Referrals were based on these classifications, followed by intraocular pressure (IOP) measurements, with rapid decisions simulating busy outpatient clinics.In the second stage, four glaucoma specialists independently categorized images as normal, suspect, or glaucomatous. Reassessments were conducted with access to IOP and contralateral eye data.

Results: In the first stage, the agreement between senior and junior specialists in categorizing patients as normal or abnormal was moderately high. Knowledge of IOP emerged as an independent factor influencing the decision to refer more patients. In the second stage, agreement among the four specialists varied, with greater concordance observed when additional clinical information was available. Notably, there was a statistically significant variability in the assessment of optic disc excavation.

Conclusion: The inclusion of various risk factors significantly influences the classification accuracy of specialists. Risk factors like IOP and bilateral data influence diagnostic consistency among specialists. Reliance solely on fundus photographs for AI training can be misleading due to inter-observer variability. Comprehensive datasets integrating multimodal clinical information are essential for developing robust AI models for glaucoma screening.