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Robot-assisted minimally invasive surgery (MIS) has gained popularity due to its high dexterity and reduced invasiveness to the patient; however, due to the loss of direct touch of the surgical site, surgeons may be prone to exert larger forces and cause tissue damage. To quantify tool-tissue interaction forces, researchers have tried to attach different kinds of sensors on the surgical tools. This sensor attachment generally makes the tools bulky and/or unduly expensive and may hinder the normal function of the tools; it is also unlikely that these sensors can survive harsh sterilization processes. This paper investigates an alternative method by estimating tool-tissue interaction forces using driving motors' current, and validates this sensorless force estimation method on a 3-degree-of-freedom (DOF) robotic surgical grasper prototype. The results show that the performance of this method is acceptable with regard to latency and accuracy. With this tool-tissue interaction force estimation method, it is possible to implement force feedback on existing robotic surgical systems without any sensors. This may allow a haptic surgical robot which is compatible with existing sterilization methods and surgical procedures, so that the surgeon can obtain tool-tissue interaction forces in real time, thereby increasing surgical efficiency and safety.
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http://dx.doi.org/10.1115/1.4032591 | DOI Listing |
BMC Surg
August 2025
NovaTimes Intelligent Technology (Chengdu) Co., Ltd, Chengdu, 610213, China.
Background: A persistent problem with robot-assisted minimally invasive surgery is soft tissue damage caused by the exertion of excessive force due to the surgeon’s lack of direct access to the surgical site. A solution to predict clamp force accurately is needed to enhance surgical safety and efficiency.
Methods: The current proposal concerns a deep learning-based solution utilizing a backpropagation neural network (BPNN) optimized by improved sparrow search algorithm (ISSA) to predict clamp force on soft tissue.
Int J Comput Assist Radiol Surg
May 2025
Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, China.
Purpose: This study aimed to develop an artificial intelligence (AI) model for the surgical report output of laparoscopic lymph node dissection in the suprapancreatic region during gastric cancer surgery.
Methods: Patients who underwent laparoscopic radical resection for gastric cancer were included in this study, and their surgical videos were analyzed. The videos were recorded from the opening of the gastropancreatic fold as the starting point to the transection of the left gastric artery as the endpoint, with the video frame rate set to 1 frame per second.
Rep U S
October 2024
Jiawei Ge, Justin D. Opfermann, and Axel Krieger are with the Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21211, USA.
In soft tissue surgeries, such as tumor resections, achieving precision is of utmost importance. Surgeons conventionally achieve this precision through intraoperative adjustments to the cutting plan, responding to deformations from tool-tissue interactions. This study examines the integration of physics-based tissue cutting simulations into autonomous robotic surgery to preoperatively predict and compensate for such deformations, aiming to improve surgical precision and reduce the necessity for dynamic adjustments during autonomous surgeries.
View Article and Find Full Text PDFIEEE Trans Med Imaging
May 2025
Four-dimensional microscope-integrated optical coherence tomography enables volumetric imaging of tissue structures and tool-tissue interactions in ophthalmic surgery at interactive update rates. This enables surgeons to undertake particular surgical steps under four-dimensional optical coherence tomography (4D OCT) guidance. However, current 4D OCT systems are limited by their field of view and signal quality.
View Article and Find Full Text PDFJ Robot Surg
October 2024
Department of Instrument Science and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China.
Robot-assisted laparoscopic surgery has three main system requirements: safety, simplicity, and intuitiveness. However, accidental movement of the endoscope due to body fatigue and misunderstanding of the verbal orders between the surgeon and assistant will contribute to highly unexpected tool-tissue interactions, particularly in pediatric minimal access surgery with restricted working space. This study introduces a compact, lightweight endoscope manipulator with a mechanical remote-center-motion function.
View Article and Find Full Text PDF