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

Area of Science:

• Medical device design
• Machine learning applications
• Biomedical engineering

Background:

• Precise navigation of medical needles in soft tissues remains a significant challenge.
• Current methods for designing needle trajectories lack adaptability and precision.

Purpose of the Study:

• To develop a generative machine learning framework for the reverse design of puncture needle deflection curves.
• To derive optimal needle tip design parameters for precise navigation in soft tissues.

Main Methods:

• A generative machine learning framework with reverse prediction and forward validation components.
• A needle-tissue interaction physics model based on Euler-Bernoulli beam theory.
• Generation of 10,000 datasets via parametric sampling and application of ensemble learning.

Main Results:

• Curve classifier achieved F1 scores from 0.945 to 0.969; deflection curve regressor showed R² of 0.9981 with 0.0008 mm average error.
• Average relative error for predicted design parameters was within 5%, reduced by 4.3% using ensemble learning.
• Validated optimal design showed excellent agreement with target trajectory (NRMSE 1.15903E-4, deviation ±0.3 mm).

Conclusions:

• The framework provides an efficient solution for precise medical needle tip design.
• This study establishes a new paradigm for intelligent medical device design.
• The developed model enables accurate prediction and optimization of needle deflection curves for targeted tissue navigation.