José Moreno

Decomposition and analysis of laparoscopic suturing task using tool-motion analysis (TMA): improving the objective assessment

PurposeThe laparoscopic suturing task is a complex procedure that requires objective assessment of surgical skills. Analysis of laparoscopic suturing task components was performed to improve current objective assessment tools.MethodsTwelve subjects participated in this study as three groups of four surgeons (novices, intermediates and experts). A box-trainer and organic tissue were used to perform the experiment while tool movements were recorded with the augmented reality haptic system. All subjects were right-handed and developed a surgeon’s knot. The laparoscopic suturing procedure was decomposed into four subtasks. Different objective metrics were applied during tool-motion analysis (TMA). Statistical analysis was performed, and results from three groups were compared using the Jonckheere–Terpstra test, considering significant differences when Pxa0≤ 0.05.ResultsSeveral first, second and fourth subtask metrics had significant differences between the three groups. Subtasks 1 and 2 had more significant differences in metrics than subtask 4. Almost all metrics showed superior task executions accomplished by experts (lower time, total path length and number of movements) compared with intermediates and novices.ConclusionThe most important subtasks during suture learning process are needle puncture and first knot. The TMA could be a useful objective assessment tool to discriminate surgical experience and could be used in the future to measure and certify surgical proficiency.

Augmented reality haptic (ARH): an approach of electromagnetic tracking in minimally invasive surgery

PurposeMinimally Invasive Surgery (MIS) is a widely used surgical technique that requires a long training process due to its difficulty and complexity. We developed an Augmented Reality Haptic (ARH) System based on electromagnetic tracking devices for use in creation training models (computer-enhanced trainers), in computer-assisted surgery or telemanipulation applications.MethodThe ARH system consists currently in a Linux driver and a calibration protocol to acquire the tooltip position of conventional laparoscopic tools in real time. A Polhemus Isotrack® II was used to track surgical endoscopic tooltip movements. The receiver was mounted on the tool handle in order to measure laparoscopic tools positions without complex modifications. Two validation tests were done to guarantee the proper functioning of the ARH system in a MIS environment. The first one checks the driver operation and the second measures the accuracy and reliability of the tooltip pose estimation process.ResultsJitter and orientation errors for the first test were 2.00±0.10 and 2.00±0.09xa0mm, respectively. Relative position error of 0.25±0.06xa0cm for a distance of 5xa0cm was found. Jitter error for the second test was 127 ± 60, 117 ± 40 and 122 ± 39xa0mm in Z, Y and X rotations, respectively.ConclusionsResults obtained with the ARH system are sufficiently accurate for use in MIS training. A supplementary correction procedure would be necessary to use this ARH system in computer-assisted surgery or telemanipulation.

Video-based assistance system for training in minimally invasive surgery.

Abstract In this paper, the development of an assisting system for laparoscopic surgical training is presented. With this system, we expect to facilitate the training process at the first stages of training in laparoscopic surgery and to contribute to an objective evaluation of surgical skills. To achieve this, we propose the insertion of multimedia contents and outlines of work adapted to the level of experience of trainees and the detection of the movements of the laparoscopic instrument into the monitored image. A module to track the instrument is implemented focusing on the tip of the laparoscopic tool. This tracking method does not need the presence of artificial marks or special colours to distinguish the instruments. Similarly, the system has another method based on visual tracking to localize support multimedia content in a stable position of the field of vision. Therefore, this position of the support content is adapted to the movements of the camera or the working area. Experimental results are presented to show the feasibility of the proposed system for assisting in laparoscopic surgical training.

Ergonomic Assessment of Hand Movements in Laparoscopic Surgery Using the CyberGlove

The main objective of this chapter is the automation of the ergonomic assessment of the wrist’s positions, through biomechanics analysis techniques, specifically using the rapid upper limb assessment (RULA). So, this allows establishing new use and design guidelines of the laparoscopic instruments, in order to reduce the influence of risk factors in the wrist area, which are associated with forced positions during the development of laparoscopic activities.

Image compression algorithm based on morphological associative memories

A new method for image compression based on Morphological Associative Memories (MAM) is proposed. We used MAM at the transformation stage of image coding, thereby replacing the traditional methods such as Discrete Cosine Transform or Wavelet Transform. After applying the MAM, the informative image data are concentrated in a minimum of values. The next stages of image coding can be obtained by taking advantage of this new representation of the image. The main advantage offered by the MAM with respect to the traditional methods is the speed of processing, whereas the compression rate and the obtained signal to noise ratios compete with the traditional methods.

Analysis of the performance of an Ultrasonic Local Positioning System based on the emission of Kasami codes

This work presents the performance analysis of an Ultrasonic Local Positioning System (ULPS). The system is composed of four beacons, forming a square structure in the ceiling of a rectangular room, that emit orthogonal Kasami codes BPSK modulated with a carrier frequency of 50 kHz. These emissions are detected by the receiver by pulse compression, giving the system high precision and robustness to noise. A complete model of the system has been built to conduct this study, considering effects such as the ultrasonic transducers response, signal attenuation in air, multipath propagation, reflection coefficient of walls and floor and receiver response. This model helps to identify critical zones where the self-induced noise generated in the cross-correlations masks the main peaks of the auto-correlations, making it difficult to obtain reliable Time-of-Flights from which the receivers position is determined. Also, the dependence of these critical zones with different parameters integrated in the model is investigated.

Reliability Evaluation Using Monte Carlo Simulation and Support Vector Machine

In this paper we propose the use of Support Vector Machine (SVM) to evaluate system reliability. The main idea is to develop an estimation algorithm by training a SVM on a restricted data set, replacing the system performance model evaluation by a simpler calculation. The proposed approach is illustrated by an example. System reliability is properly emulated by training a SVM with a small amount of information.

Volume Reconstruction for Health Care

Abstract: In many scientific and technical applications, a three‐dimensional (3D) object must be reconstructed, either to assist in understanding the objects structure or to ease its automatic handling and analysis. Volume reconstruction has been used in health care to diagnose, simulate, facilitate surgical planning, develop image‐guided surgery, facilitate telemedicine, and to assist in many other applications. This paper presents a survey of computational methods used to achieve volume reconstruction. First, we review 3D imaging techniques. Second, since we consider image segmentation the most important and difficult phase of 3D reconstruction, we focus on this topic. Subsequently, we concentrate on some of the most successful techniques of segmentation used for 3D reconstruction, such as active contours. We also review computer graphics and visualization methods used with volume reconstruction. Finally, we indicate future lines for research in volume reconstruction and 3D imaging for health care.

Machine Learning Models for Online Dynamic Security Assessment of Electric Power Systems

In this paper we compare two machine learning algorithms (Support Vector Machine and Multi Layer Perceptrons) to perform on-line dynamic security assessment of an electric power system. Dynamic simulation is properly emulated by training SVM and MLP models, with a small amount of information. The experiments show that although both models produce reasonable predictions, the performance indexes of the SVM models are better than those of the MLP models. However the MLP models are of considerably reduced complexity.

Technical Evaluation of a Third Generation Optical Pose Tracker for Motion Analysis and Image-Guided Surgery

Laparoscopic instrument tracking systems are an essential component in image-guided interventions and offer new possibilities to improve and automate objective assessment methods of surgical skills. In this study we present our system design to apply a third generation optical pose tracker (Micron- Tracker®) to laparoscopic practice. A technical evaluation of this design is performed in order to analyze its accuracy in computing the laparoscopic instrument tip position. Results show a stable fluctuation error over the entire analyzed workspace. The relative position errors are 1.776±1.675 mm, 1.817±1.762 mm, 1.854±1.740 mm, 2.455±2.164 mm, 2.545±2.496 mm, 2.764±2.342 mm, 2.512±2.493 mm for distances of 50, 100, 150, 200, 250, 300, and 350 mm, respectively. The accumulated distance error increases with the measured distance. The instrument inclination covered by the system is high, from 90 to 7.5 degrees. The system reports a low positional accuracy for the instrument tip.