Abstract
OBJECTIVES: To address the absence of a systematic evaluation method for network architecture selection in ophthalmic ultrasound image detection tasks, this study proposes a modular ablation analysis framework based on orthogonal experimental design. METHODS: A clinical data set comprising 1121 ocular ultrasound images was established. YOLOv10-v12 were decoupled into backbone, neck, and head modules. A three-stage evaluation was conducted: (1) single-module benchmarking, performed via controlled variable experiments; (2) orthogonal combination experiments using an L9(3(4)) array, analyzed through range analysis and interaction heatmaps; and (3) optimal architecture selection, implemented via Pareto front analysis. The best model was applied to ocular tissue localization, and a segmented sound velocity matching algorithm was used to automatically measure biometric parameters, including anterior chamber depth, lens thickness, and axial length. RESULTS: The backbone improved both accuracy and efficiency, while the neck and head exhibited a speed-accuracy trade-off. The neck most significantly influenced detection accuracy, and the head dominated computational efficiency. The optimal combination (Bv11-Nv11-Hv10) achieved 64.0% mAP at 26 FPS, while the mobile-optimized variant (Bv10-Nv10-Hv11) attained 63.5% mAP with only 8.6 MB parameters. Automatic and manual measurements showed strong agreement (mean absolute error ≤ 0.133 mm, ICC ≥ 0.839). CONCLUSIONS: This study validates the feasibility of cross-version module combination. The proposed framework offers a systematic, quantitative decision-making basis for network design in ophthalmic ultrasound, balancing accuracy, speed, and deployment feasibility. Clinical results confirm high consistency between automatic and manual measurements, supporting automated and precise ocular biometry.