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New research · Ophthalmology
Cureus · 5d
AI / informaticsCureus · 2026

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

Krishna Keshav, Deepsekhar Das, Sumit Grover … Avinav Bharti
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OphthalmologyAI / informatics

Mixing synthetic and real eye images gives artificial intelligence stable, accurate pupil detection.

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

Krishna Keshav … Avinav Bharti
Cureus · 2026
Background

This study aimed to evaluate whether mixing synthetic and real eye images for artificial intelligence (artificial intelligence) training improves cross-domain segmentation of the sclera, iris, and pupil compared to single-domain datasets.

Purpose

This study aimed to evaluate whether mixing synthetic and real eye images for artificial intelligence (artificial intelligence) training improves cross-domain segmentation of the sclera, iris, and pupil compared to single-domain datasets.

Methods

Four Roboflow 3.0 instance segmentation models were trained: (1) 100 artificial intelligence-generated images, (2) 100 real images, (3) 50:50 mixed, n = 100, and (4) 50:50 mixed, n = 200.

n = 200 images
Results
88.5%
pupil detected correctly in nearly 9 of 10 test images
n = 200 images
More 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: artificial intelligence-trained 0% on one real image; real-trained 50% on one artificial intelligence image.

More results

Doubling mixed data to 200 images gave no added benefit (p = 0.95).

“
Conclusion

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 artificial intelligence.

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