Automatic cutting and suturing control system based on improved FP16 visual recognition algorithm
Objective This study aims to develop and evaluate an autonomous surgical system based on the Toumai laparoscopic surgical robot, focusing on improving the precision and reliability of automated cutting and suturing operations. Methods The proposed system integrates several key components: (1) Roboti...
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| Huvudupphovsmän: | , |
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| Materialtyp: | Artikel |
| Språk: | engelska |
| Publicerad: |
SAGE Publishing
2025-07-01
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| Serie: | International Journal of Advanced Robotic Systems |
| Länkar: | https://doi.org/10.1177/17298806251348118 |
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| Sammanfattning: | Objective This study aims to develop and evaluate an autonomous surgical system based on the Toumai laparoscopic surgical robot, focusing on improving the precision and reliability of automated cutting and suturing operations. Methods The proposed system integrates several key components: (1) Robotic arms and associated control systems. (2) An endoscopic system supporting advanced visual image algorithms. (3) Specialized surgical instruments for cutting and suturing. A binocular stereo matching algorithm is employed to obtain depth information from the field of binocular camera. The DarkPose image key point localization algorithm and the Yolov5 image detection algorithm are utilized to accurately determine the positions of surgical instruments, suture needles, and target points. Additionally, an image classification discriminator is introduced to assess the success of the surgical tasks. A finite state machine model is used to guide the robotic arm's end-effector through real-time trajectory planning and execution, ensuring precise completion of surgical tasks. Results Experimental evaluation demonstrated that the autonomous system achieves high precision and reliability in both cutting and suturing tasks. Quantitative analysis shows that the system maintains an 85% success rate in automatic cutting, with a mean time of 5.10 s per cutting action. The automatic suturing task achieves a 92% accuracy rate in instrument positioning and a 90% success rate in needle grasping. Conclusion The developed system shows significant promise in automating key laparoscopic surgical tasks, with the potential to enhance surgical efficiency and improve outcomes in clinical practice. Further development and validation of this system could lead to its broader adoption in the field of autonomous surgery. |
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| ISSN: | 1729-8814 |