ArticleSensors (Basel, Switzerland)2020
Vision-Based Suture Tensile Force Estimation in Robotic Surgery.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- A Two-Stage Grip Force Estimation Method for Surgical Robots Based on BiLSTM-iTransformer.Annals of the New York Academy of Sciences · 2026Article
- Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool-Tissue Force Prediction from Laparoscopic Depth Maps.Bioengineering (Basel, Switzerland) · 2026Article
- Exploring video recognition models for force estimation in small bowel surgical retractions.International journal of computer assisted radiology and surgery · 2026Article
- Force in robotic thoracic surgery -a one year analysis of DaVinci 5 force feedback.Journal of robotic surgery · 2025Article
- Precision at scale: Machine learning revolutionizing laparoscopic surgery.World journal of clinical oncology · 2024Article
- Evaluation of forces applied to tissues during robotic-assisted surgical tasks using a novel force feedback technology.Surgical endoscopy · 2024Article
- Visualisation ergonomics and robotic surgery.Journal of robotic surgery · 2023Review
- A Modular 3-Degrees-of-Freedom Force Sensor for Robot-Assisted Minimally Invasive Surgery Research.Sensors (Basel, Switzerland) · 2023Article
- A Transparent Teleoperated Robotic Surgical System with Predictive Haptic Feedback and Force Modelling.Sensors (Basel, Switzerland) · 2022Article
- Sensors Technology for Medical Robotics.Sensors (Basel, Switzerland) · 2022Article
- Performance and Capability Assessment in Surgical Subtask Automation.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Compared to laparoscopy, robotics-assisted minimally invasive surgery has the problem of an absence of force feedback, which is important to prevent a breakage of the suture. To overcome this problem, surgeons infer the suture force from their proprioception and 2D image by comparing them to the training experience. Based on this idea, a deep-learning-based method using a single image and robot position to estimate the tensile force of the sutures without a force sensor is proposed. A neural network structure with a modified Inception Resnet-V2 and Long Short Term Memory (LSTM) networks is used to estimate the suture pulling force. The feasibility of proposed network is verified using the generated DB, recording the interaction under the condition of two different artificial skins and two different situations (in vivo and in vitro) at 13 viewing angles of the images by changing the tool positions collected from the master-slave robotic system. From the evaluation conducted to show the feasibility of the interaction force estimation, the proposed learning models successfully estimated the tensile force at 10 unseen viewing angles during training.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.