Xinyao Li, Gaoshuai Chen, Xin Yang, Yuchao Liu, Yu Xia

The School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Xinyao Li, The School of Mechanical Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu District, Shanghai 200093, China. Tel: +86-15156575887; E-mail: 15156575887@163.com.

DOI:https://doi.org/10.61189/143103ncllxn

Received November 15, 2024; Accepted July 11, 2025; Published September 30, 2025

Highlights

● Proposed an improved SVD algorithm to reduce errors caused by electromagnetic interference, thereby enhancing the precision of surgical navigation systems.

● Validated the algorithm through puncture experiments on biomimetic tissue of angles 0°, 5°, and 10° , demonstrating high accuracy in simulating tumor localization.

Yu Zhou¹ , Shiju Yan¹ , Ailing Zhang²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China. ²College of Health Management, Shanghai Jian Qiao University, Shanghai 201306, China.

Address correspondence to: Ailing Zhang, College of Health Management, Shanghai Jian Qiao University, NO. 1111 Hucheng Ring Road, Pudong New Area, Shanghai 201306, China, Tel: +86-18616022898. E-mail: zhangailing@gench.edu.cn.

DOI: https://doi.org/10.61189/063815kuibzu

Received January 22, 2025; Accepted March 13, 2025; Published September 30, 2025

Highlights

● A novel testing device for measuring the radial support force and bending spring-back force of stent grafts has been developed.

● A theoretical geometric model for the bending behavior of stent grafts was derived and applied in numerical simulations, significantly enhancing the simulation results.

● The device’s reliability was validated by comparing numerical simulation outcomes with physical experimental results under varying compression diameters and bending angles.

Linrong Yuan^* , Yutong Xie^* , Danhong Li^* , Jianghui Li, Miao Yu, Siqi Wang, Yu Wang, He Ren

Faculty of Medical Instrumentation, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China.

^* The authors contribute equally.

Address correspondence to: He Ren, Faculty of Medical Instrumentation, Shanghai University of Medicine and Health Sciences; No. 279, Zhouzhu Highway, Pudong New Area, Shanghai 201318, China. Tel: +86-18817581363. E-mail: renh@sumhs.edu.cn.

DOI: https://doi.org/10.61189/941872mmikqi

Received July 26, 2025; Accepted September 10, 2025; Published September 30, 2025

Highlights

● A total of 289 patient CT datasets were analyzed, and 15 optimal radiomic features were identified using ANOVA, correlation analysis, and random forest ranking, ensuring high discriminative power and clinical interpretability.

● The proposed model demonstrated excellent performance (Accuracy: 0.98, Area Under the Curve: 0.99) in training set, demonstrating robust learning capacity and the ability to distinguish lesion subtypes from multidimensional radiomic features.

● By leveraging serialized radiomic trends rather than isolated feature analysis, this study provides a new paradigm for early screening and personalized diagnosis of lung adenocarcinoma.