Nondestructive testing and validation of additively manufactured eye phantoms for ophthalmic applications

Ophthalmic phantoms are essential for surgical training, device validation, and preclinical research, requiring accurate anatomical structures and realistic biomechanical properties without compromising their integrity through destructive testing. This study proposes the Nondestructive Ophthalmic Validation Algorithm of Eye Phantoms (NOVA-EyeP), a multimodal framework for the objective evaluation of 3D-printed ophthalmic phantoms. The proposed approach integrates optical coherence tomography, ultrasound imaging, volumetric inspection, and non-contact mechanical response analysis to acquire complementary structural and biomechanical information. An adaptive multimodal fusion strategy combines modality-specific reliability with anatomical significance to generate the Ophthalmic Validation Index (OVI) and Surgical Readiness Score (SRS) for comprehensive assessment. Experimental evaluation on 500–1000 multimodal samples achieved a Dice coefficient of 0.88, defect detection accuracy of 91.3%, and biomechanical prediction error of ±6.2%. Comparative analysis against FEM-based Simulation, GNN, U-Net, Ultrasound-Only, and PINN methods demonstrated superior performance, achieving 94% accuracy, 92% sensitivity, 96% specificity, 0.018 mm MAE, 0.022 mm RMSE, 4.2% MAPE, and an ICC of 0.93. These results demonstrate that NOVA-EyeP provides a reliable, objective, and nondestructive framework for ophthalmic phantom validation, enhancing surgical training, simulation, and ophthalmic device evaluation.