Mixing Synthetic and Real Images Improves Artificial Intelligence-Based Detection of the Pupil, Iris, and Sclera: A Cross-Domain Validation Study

Background This study aimed to evaluate whether mixing synthetic and real eye images for artificial intelligence (AI) training improves cross-domain segmentation of the sclera, iris, and pupil compared to single-domain datasets. Methodology Four Roboflow 3.0 instance segmentation models were trained: (1) 100 AI-generated images, (2) 100 real images, (3) 50:50 mixed, n = 100, and (4) 50:50 mixed, n = 200. All were tested on five AI-generated and five real eye images. Detection accuracy was calculated per structure. Performance was compared using paired t-tests and two-way analysis of variance. Results Mixed models eliminated domain-specific failures. Pupil accuracy showed a significant training × test domain interaction (p = 0.003), with single-domain models failing on opposite domains: AI-trained 0% on one real image; real-trained 50% on one AI image. The 100-Mix model achieved 88.5% ± 5.9% pupil accuracy with no failures, with a standard deviation of <6% versus 29.8% for AI-only. Doubling mixed data to 200 images gave no added benefit (p = 0.95). Conclusions Hybrid training with 50:50 synthetic-real images achieves robust, domain-stable detection of the pupil, iris, and sclera. Mixed datasets, not larger datasets, are key for clinically deployable ocular AI.