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Advances in Intelligent Minimally Invasive Surgical Robots
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Advances in Intelligent Minimally Invasive Surgical Robots

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 3424

Special Issue Editors

Dr. Irene Rivas Blanco

E-Mail Website
Guest Editor
Department of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Malaga, Spain
Interests: medical robotics; machine learning; control systems
Dr. Carlos J. Pérez Del Pulgar

E-Mail Website
Guest Editor
Department of Systems Engineering and Automation, Universidad de Málaga, Andalucía Tech, 29071 Malaga, Spain
Interests: space robotics; machine learning; path and motion planning; control systems for space
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, minimally invasive procedures have become common practice in many surgical interventions, with huge benefits for patients and physicians. This advance goes hand-in-hand with the great technical advances of medical robots in recent years. Medical robots have allowed us to reduce the invasiveness of surgeries by providing more sophisticated tools to operate, with higher accuracy and range of motion. But the benefits of this new generation of medical devices are not limited to their superior movement capacity. Improvements in machine learning techniques are providing medical robots with more decision making and autonomy skills. Thus, medical robots are beginning to play an active part in operating theatres as their capacity for analyzing and understanding the medical environment increases.

The aim of this Special Issue is to advance medical robot research, the automation of medical procedures, surgical scene understanding and decision making, surgical skill assessment, new medical devices, and related areas. Topics of interest include, but are not limited to, the following:

  • Intelligent medical devices;
  • Autonomous surgical tasks;
  • Machine learning in minimally invasive procedures;
  • Medical imaging for surgical scene understanding;
  • The design of new advanced medical devices;
  • Human–robot interfaces for medical procedures.

Dr. Irene Rivas Blanco
Dr. Carlos J. Pérez Del Pulgar
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • minimally invasive surgery
  • surgical robots
  • artificial intelligence
  • machine learning
  • control strategies
  • surgical image analysis
  • surgical task analysis
  • surgical skill assessment
  • surgical task automation

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Published Papers (3 papers)

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Research

17 pages, 7204 KiB  
Article
A Novel, Soft, Cable-Driven Parallel Robot for Minimally Invasive Surgeries Based on Folded Pouch Actuators
by Jianlin Yang, Xinxin Li, Mark Runciman, James Avery, Zhangxi Zhou, Zhijun Sun and George Mylonas
Appl. Sci. 2024, 14(10), 4095; https://doi.org/10.3390/app14104095 - 11 May 2024
Viewed by 1271
Abstract
This paper introduces a soft, cable-driven parallel robot for minimally invasive surgeries. The robot comprises a pneumatic inflatable scaffold, six hydraulic, folded pouch actuators, and a hollow, cylindrical end-effector offering five degrees of freedom. A key development is the design of the pouch [...] Read more.
This paper introduces a soft, cable-driven parallel robot for minimally invasive surgeries. The robot comprises a pneumatic inflatable scaffold, six hydraulic, folded pouch actuators, and a hollow, cylindrical end-effector offering five degrees of freedom. A key development is the design of the pouch actuators, which are small, low-profile, simple structures, capable of a high stroke of 180° angular displacement. The scaffold, actuators, and plastic cables are economically and rapidly fabricated using laser cutting and welding techniques. Constructed primarily from soft plastic materials, the robot can be compactly folded into a cylinder measuring 110 mm in length and 14 mm in diameter. Upon inflation, the scaffold transforms into a hexagonal prism structure with side lengths of 34 mm and edge lengths of 100 mm. The kinematic model of the robot has been developed for workspace calculation and control purposes. A series of tests have been conducted to evaluate the performance of the actuator and the robot. Repeatability tests demonstrate the robot’s high repeatability, with mean and root mean square errors of 0.3645 mm and 0.4186 mm, respectively. The direct connection between the end-effector and the actuators theoretically eliminates cable friction, resulting in a hysteresis angle of less than 2°, as confirmed by the tracking results. In addition, simulated surgical tasks have been performed to further demonstrate the robot’s performance. Full article
(This article belongs to the Special Issue Advances in Intelligent Minimally Invasive Surgical Robots)
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25 pages, 4673 KiB  
Article
Instrument Detection and Descriptive Gesture Segmentation on a Robotic Surgical Maneuvers Dataset
by Irene Rivas-Blanco, Carmen López-Casado, Juan M. Herrera-López, José Cabrera-Villa and Carlos J. Pérez-del-Pulgar
Appl. Sci. 2024, 14(9), 3701; https://doi.org/10.3390/app14093701 - 26 Apr 2024
Viewed by 936
Abstract
Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA [...] Read more.
Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend the ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low-level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tools and the left-handed tools. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. On the other hand, for gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP. Full article
(This article belongs to the Special Issue Advances in Intelligent Minimally Invasive Surgical Robots)
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11 pages, 8934 KiB  
Article
Neural Tract Avoidance Path-Planning Optimization: Robotic Neurosurgery
by Juliana Manrique-Cordoba, Carlos Martorell, Juan D. Romero-Ante and Jose M. Sabater-Navarro
Appl. Sci. 2024, 14(9), 3687; https://doi.org/10.3390/app14093687 - 26 Apr 2024
Viewed by 858
Abstract
Background: We propose a three-dimensional path-planning method to generate optimized surgical trajectories for steering flexible needles along curved paths while avoiding critical tracts in the context of surgical glioma resection. Methods: Our approach is based on an application of the rapidly exploring random [...] Read more.
Background: We propose a three-dimensional path-planning method to generate optimized surgical trajectories for steering flexible needles along curved paths while avoiding critical tracts in the context of surgical glioma resection. Methods: Our approach is based on an application of the rapidly exploring random tree algorithm for multi-trajectory generation and optimization, with a cost function that evaluates different entry points and uses the information of MRI images as segmented binary maps to compute a safety trajectory. As a novelty, an avoidance module of the critical neuronal tracts defined by the neurosurgeon is included in the optimization process. The proposed strategy was simulated in real-case 3D environments to reach a glioma and bypass the tracts of the forceps minor from the corpus callosum. Results: A formalism is presented that allows for the evaluation of different entry points and trajectories and the avoidance of selected critical tracts for the definition of new neurosurgical approaches. This methodology can be used for different clinical cases, allowing the constraints to be extended to the trajectory generator. We present a clinical case of glioma at the base of the skull and access it from the upper area while avoiding the minor forceps tracts. Conclusions: This path-planning method offers alternative curved paths with which to reach targets using flexible tools. The method potentially leads to safer paths, as it permits the definition of groups of critical tracts to be avoided and the use of segmented binary maps from the MRI images to generate new surgical approaches. Full article
(This article belongs to the Special Issue Advances in Intelligent Minimally Invasive Surgical Robots)
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