Generative machine learning-based framework for reverse design of puncture needle deflection curves

This study introduces a generative machine learning-based framework for the reverse design of puncture needle deflection curves, aiming to address the challenge of precise navigation of puncture needles in soft tissues. The framework consists of two primary components: reverse prediction and forward validation, which work together to derive optimal needle tip design parameters from the target deflection curve. A needle-tissue interaction physics model is developed based on Euler-Bernoulli beam theory, and 10,000 datasets are generated through a parametric sampling method. Experimental results demonstrate that the curve classifier achieves an F1 score ranging from 0.945 to 0.969, while the deflection curve regressor shows an R² value of 0.9981 with an average error of only 0.0008 mm. The average relative error of the predicted design parameters is kept within 5%. Furthermore, an innovative ensemble learning strategy is employed, which leads to a further reduction in the prediction error by 4.3%. In cases involving high-deflection curves, the optimal design (d = 1.01 mm, L = 113.84 mm, θ = 39.08°) was validated via finite element analysis and showed excellent agreement with the target trajectory, with an NRMSE value of 1.15903E - 4 and deviation controlled within ±0.3 mm. This study not only provides an efficient solution for the precise design of medical needle tips but also establishes a new research paradigm for the intelligent design of medical devices.