Machine learning-driven inverse design of puncture needles with tailored mechanics.
Background: In minimally invasive surgery, designing puncture needles with customizable structures to achieve personalized puncture performance is a significant challenge. Existing reverse design methods struggle to capture the complex nonlinear behavior of needle-tissue interactions. Methods: This...
| Publicado en: | Minimally Invasive Therapy & Allied Technologies Vol. 35; no. 1; pp. 18 - 28 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191254991&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191254991 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13645706 J4S jtl: Minimally Invasive Therapy & Allied Technologies issn: 13645706 maglogo: Y pubinfo: dt: Feb2026 vid: 35 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 191254991 186831402 191254991 191254991 10.1080/13645706.2025.2537927 191254991 ppf: 18 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning-driven inverse design of puncture needles with tailored mechanics. aug: au: Huang, Yaozong Zhang, Fan Zhang, Fanyang Wu, Xin Xinye, Yufei affil: Laboratory of Intelligent Control and Robotics, Shanghai University of Engineering Science, Shanghai, People's Republic of China sug: subj: Machine Learning Needles Computer-Aided Design Methods Mechanics Evaluation Minimally Invasive Procedures Finite Element Analysis Precision Technology, Medical Neural Networks (Computer) Descriptive Statistics Deep Learning ab: Background: In minimally invasive surgery, designing puncture needles with customizable structures to achieve personalized puncture performance is a significant challenge. Existing reverse design methods struggle to capture the complex nonlinear behavior of needle-tissue interactions. Methods: This study proposes a machine-learning-based reverse design method aimed at achieving precise customization of needle mechanical behavior. We developed a rapid reverse design framework integrating machine learning and finite element analysis, capable of directly generating optimal structural parameters from target puncture force–penetration depth curves. Through training on large-scale finite element simulation data, deep learning neural network models captured the complex mapping relationship between needle structure and mechanical response. Results: In rigorous cross-validation, the prediction results showed normalized root mean square errors (NRMSE) of 0.06381 and 0.06234 compared to the target curves and finite element analysis, respectively. The model achieved 98.2% classification accuracy for curve types, with loss functions converging to optimal values after sufficient training epochs. Conclusion: This approach demonstrates high accuracy and robustness in needle-design customization. It not only opens new avenues for rapid, customized design of puncture needles but also provides an innovative paradigm for intelligent design of complex medical devices, potentially advancing precision medicine technologies and shortening design cycles. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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