Synthetic Generation from Real LASIK Procedures (TENEO 317 model 1 / ZYWAVE 3 / ORBSCAN 3, 2017–2024)
Abstract
DATASET TITLE TabDDPM-Generated Synthetic LASIK Biometric Dataset for Refractive Surgery AI Model Development SUMMARY This dataset comprises 25,000 synthetic patient records generated via Tabular Denoising Diffusion Probabilistic Model (TabDDPM) from a real clinical cohort of 2,448 LASIK procedures performed between July 2017 and August 2024. The synthetic data preserves the joint distribution and multivariate dependencies of real biometric measurements while ensuring strict patient anonymity and topological novelty (volumetric expansion ratio = 1.03). CLINICAL CONTEXT Preoperative and postoperative biometric measurements were acquired using three complementary diagnostic modalities: - Corneal morphometry and scanning-slit elevation topography (ORBSCAN 3, n=67 features) - Ocular aberrometry via Shack-Hartmann wavefront sensing (ZYWAVE 3, n=40 features, cycloplegia) - Excimer laser console parameters (TENEO 317 Model 1/2, n=40 features) GENERATIVE PROCESS The synthetic cohort was generated via Bayesian-optimized TabDDPM to maximize statistical fidelity (KSD=0.05, AUC-ROC=0.55) while preventing memorization of source records (DCR mean=1.821, NNDR mean=0.908, 0% exact match rate). The model successfully synthesized 1,334 novel rare clinical cases beyond the empirical convex hull of the training distribution, validating volumetric expansion into biologically plausible but unobserved phenotypes. VALIDATION METRICS - Kolmogorov-Smirnov Distance (marginal fidelity): 0.05 -…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 95,000 participants or records.
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