A Digital Twin–Inspired Patient-Specific Eye–Lens Simulation Framework for Pediatric Postcycloplegic Spectacle Prescription: A Prospective Agreement Study

Purpose: To evaluate the agreement of a digital twin-inspired patient-specific eye-lens simulation framework and reference-standard subjective refraction for pediatric postcycloplegic spectacle prescription. Methods: In this prospective randomized testing sequence agreement study, 183 right eyes from 183 children (mean age, 9.9 ± 2.0 years) underwent traditional autorefraction, artificial intelligence (AI) wavefront refraction, and masked subjective refraction. The framework used microelectromechanical systems (MEMS) Shack-Hartmann wavefront data, image quality gating, virtual trial-lens simulation, and perturbation-aware optimization to generate an AI-based prescription estimate. Agreement was assessed for spherical equivalent (SE), Jackson cross-cylinder component at axis 0° (J0), and Jackson cross-cylinder component at axis 45° (J45). Results: Traditional autorefraction differed significantly from subjective refraction in SE, J0, and J45: 0.21 ± 0.52 diopters (D) for SE (P < 0.001), -0.04 ± 0.21 D for J0 (P = 0.006), and 0.02 ± 0.13 D for J45 (P = 0.037). AI wavefront refraction showed smaller mean differences without statistically significant differences: 0.04 ± 0.35 D for SE (P = 0.153), -0.01 ± 0.19 D for J0 (P = 0.681), and 0.00 ± 0.15 D for J45 (P = 0.725). For SE, AI wavefront refraction showed narrower 95% limits of agreement than traditional autorefraction (-0.67 to 0.74 D vs. -0.81 to 1.22 D) and higher proportions within ≤0.25 D and ≤0.50 D (66.1% vs. 54.6%; 91.3% vs. 86.9%). Conclusions: The digital twin-inspired patient-specific eye-lens simulation framework generated pediatric postcycloplegic prescription estimates that agreed more closely with subjective refraction than traditional autorefraction, particularly for SE. These findings support its feasibility as an upstream decision support component, although real-world spectacle wear feedback and longitudinal model updating require further study. Translational Relevance: This study provides prospective clinical validation for an upstream component of a future human-in-the-loop ophthalmic digital twin for spectacle prescription by translating patient-specific wavefront data into virtual trial-lens simulation and AI-assisted refraction decision support.