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Applied Sciences
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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 768

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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

Published Papers (2 papers)

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Research

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 272
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 304
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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