Binocular Vision-Assisted Magnetic Soft Catheter Robot System for Minimally Invasive in-Situ Bioprinting

Magnetic soft catheter (MSC) robots, renowned for their remarkable flexibility and wireless controllability, are suitable for operation in constrained and dynamic in vivo environments, and they have shown application potential for in-situ bioprinting. Nonetheless, this type of in-situ bioprinting systems currently lack intraoperative intelligent defect reconstruction (DR) techniques, relying solely on preoperative imaging to guide printing, thereby introducing considerable uncertainty into the in vivo printing procedure. Here, a binocular vision-assisted MSC robot system (BV-MSCRS) is reported to perform automatic in-situ bioprinting with minimal incision. A DR algorithm based on deep learning and binocular stereo matching technology is developed to intelligently reconstruct the 3D positions and morphologies of the target defects, thereby assisting in-situ bioprinting. In accordance with the obtained 3D reconstruction information, a magnetic control strategy is designed to actuate the MSC and execute in-situ bioprinting tasks by five orthogonal electromagnets. The effectiveness of the proposed BV-MSCRS have been verified by in-vitro printing experiments on planar surfaces and in-situ bioprinting experiments on a cartilage defect of an isolated porcine femur. This study aspires to advance minimally invasive in-situ bioprinting technology toward intelligence and automation, exploring its potential in a diversity of clinical applications, such as repair of damage on cartilage, muscle, gastric wall and liver.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2024-10-03
DOI
https://doi.org/10.1109/lra.2024.3474552
Citations
6
Primary Topic
Retinal Imaging and Analysis
Type
article
Field-Weighted Citation Impact
2.43

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article

Binocular Vision-Assisted Magnetic Soft Catheter Robot System for Minimally Invasive in-Situ Bioprinting

Songyu Hu, Yong He, An Liu, Jianzhong Fu et al.
6 citations
IEEE Robotics and Automation Letters
Retinal Imaging and Analysis
2.43
article

Binocular Vision-Assisted Magnetic Soft Catheter Robot System for Minimally Invasive in-Situ Bioprinting

Songyu Hu, Yong He, An Liu, Jianzhong Fu, Jiaying Hu, Jiarong Hu, Liwen Guo, Wangxie Gu
article en
6 citations

Abstract

Magnetic soft catheter (MSC) robots, renowned for their remarkable flexibility and wireless controllability, are suitable for operation in constrained and dynamic in vivo environments, and they have shown application potential for in-situ bioprinting. Nonetheless, this type of in-situ bioprinting systems currently lack intraoperative intelligent defect reconstruction (DR) techniques, relying solely on preoperative imaging to guide printing, thereby introducing considerable uncertainty into the in vivo printing procedure. Here, a binocular vision-assisted MSC robot system (BV-MSCRS) is reported to perform automatic in-situ bioprinting with minimal incision. A DR algorithm based on deep learning and binocular stereo matching technology is developed to intelligently reconstruct the 3D positions and morphologies of the target defects, thereby assisting in-situ bioprinting. In accordance with the obtained 3D reconstruction information, a magnetic control strategy is designed to actuate the MSC and execute in-situ bioprinting tasks by five orthogonal electromagnets. The effectiveness of the proposed BV-MSCRS have been verified by in-vitro printing experiments on planar surfaces and in-situ bioprinting experiments on a cartilage defect of an isolated porcine femur. This study aspires to advance minimally invasive in-situ bioprinting technology toward intelligence and automation, exploring its potential in a diversity of clinical applications, such as repair of damage on cartilage, muscle, gastric wall and liver.

IEEE Robotics and Automation LettersVol. 9(12)
Second Affiliated Hospital of Zhejiang University (CN), Zhejiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 11%
Retinal Imaging and Analysis
2.43
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