Abelardo Escoto

Force/position-based modular system for minimally invasive surgery

The limitations of minimally invasive surgery include the inability to sense forces exerted by the instruments on tissue and the limited visual cues available through the endoscope. A modular laparoscopic instrument capable of measuring force and position has been designed to address these limitations. Novel image-based position tracking software has been developed and integrated within a graphical user interface. This modular system is low cost, versatile, and could be used for training, localization of critical features or for guidance during surgical procedures.

A multi-sensory mechatronic device for localizing tumors in minimally invasive interventions

Tumor localization in traditional lung resection surgery requires manual palpation of the deflated lung through a thoracotomy. It is a painful procedure that is not suitable for many patients. Therefore, a multisensory mechatronic device was designed to localize tumors using a minimally invasive approach. The device is sensorized with tactile, ultrasound and position sensors in order to obtain multimodal data of soft tissue in real time. This paper presents the validation of the efficiency and efficacy of this device via an ex vivo experimental study. Tumor pathology was simulated by embedding iodine-agar phantom tumors of varying shapes and sizes into porcine liver tissue. The device was then used to palpate the tissue to localize and visualize the simulated tumors. Markers were then placed on the location of the tumors and fluoroscopic imaging was performed on the tissue in order to determine the localization accuracy of the device. Our results show that the device localized 87.5% of the tumors with an average deviation from the tumor center of 3.42 mm.

A knee arthroscopy simulator: Design and validation

Many challenges exist when teaching and learning arthroscopic surgery, carrying a high risk of damaging the joint during the learning process. To minimize risk, the use of arthroscopy simulators allows trainees to learn basic skills in a risk-free environment before entering the operating room. A high-fidelity physical knee arthroscopy simulator is proposed to bridge the gap between surgeons and residents. The simulator is composed of modular and replaceable elements and can measure applied forces, instrument position and hand motion, in order to assess performance in real time. A construct validity study was conducted in order to assess the performance improvement of novices after practicing with the simulator. In addition, a face validity study involving expert surgeons indicated that the simulator provides a realistic scenario suitable for teaching basic skills. Future work involves the development of better metrics to assess user performance.

Energy-Based Metrics for Arthroscopic Skills Assessment

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Low-cost force-sensing arthroscopic tool using threaded fiber Bragg grating sensors

Minimally-invasive surgery has revolutionized many medical procedures; however, it also impedes the ability to feel the interaction between the surgical tool and the anatomical part being operated on. In order to address this problem, it is necessary to obtain accurate measurements of the interaction forces exerted on the surgical tools during surgery. These forces can then be manifested to the surgeon via a haptic device or presented visually (visual-force feedback). This paper describes the use of a fiber optic device to measure and display to the surgeon interaction forces acting on an arthroscopic tool. The sensorization of the tool involves a simple, highly efficient and robust design and is ideally suited for use in a surgical training environment aimed at narrowing the gap between trainees and expert surgeons before the trainees proceed to their first surgery in vivo. The major advantages of using fiber optics include their small size, their local simplicity, their ease of sterilization, and their high sensitivity. In this paper, a complete low-cost sensing solution is described, including 1) fiber Bragg grating sensors, 2) high resolution electronic signal processing, 3) fabrication of the tool using a wire electrical discharge machine (EDM) and 3D metal sintering technologies. Experimental results demonstrate the accuracy and performance of the sensorized tool.

A wearable mechatronic brace for arm rehabilitation

In recent years, the possibility of using smart technologies to enhance rehabilitative therapies has become a reality. Smart technologies can adjust their functionality based on real-time performance to provide the most effective therapy. This paper presents the design, development and testing of a wearable mechatronic brace created to assist in upper limb rehabilitation. The purpose of the smart brace is to provide safe therapy of musculoskeletal disorders, in particular brachial plexus injuries. A control system has been developed that facilitates the retraining of the biceps for individuals who have suffered brachial plexus nerve damage. Electromyography (EMG) data for flexion and extension of the elbow were recorded from three healthy subjects and used to scale velocity profiles. The experiments assessed the performance of the smart brace in its ability to reproduce a motion, to compensate for the effect of muscle disability and to detect fatigue. The results showed that the control system was able to adjust velocities to accommodate for disability or fatigue. This initial implementation provides a control model and logic from which the brace can be improved. Future testing of the brace using subjects with a brachial plexus injury will help solidify the techniques used for brace control.

A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images

Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation (r = −0.783, p = 0.004) between the tumor surface margins and a good positive correlation (r = 0.743, p = 0.009) between the tumor depth margins.

A sterilizable force-sensing instrument for laparoscopic surgery

Although some technologies have been developed to measure tool-tissue interaction forces during minimally invasive surgery (MIS), none of these technologies have been approved for use in humans. The primary factor preventing the use of sensorized instruments in humans is their inability to withstand the stringent conditions present during cleaning and sterilization. This paper presents a series of experiments that were performed to develop a sterilizable instrument capable of measuring tool-tissue interaction forces in three degrees of freedom using strain gauges. The experiments provided an appropriate choice of cables and connectors, as well as an optimal combination of strain gauge adhesives and coatings that allow the sensors to withstand autoclave sterilization. A prototype of the sensorized instruments was developed and tested. The final prototype was able to withstand a sterilization cycle with excellent results (0.10-0.21 N accuracy, 0.05-0.20 N repeatability and 0.06-0.21 N hysteresis depending on the measurement direction). This work shows that autoclave sterilization is possible for a strain-gauge instrumented device.

Development of a Physical Shoulder Simulator for the Training of Basic Arthroscopic Skills

Orthopaedic training programs are incorporating arthroscopic simulations into their residency curricula. There is a need for a physical shoulder simulator that accommodates lateral decubitus and beach chair positions, has realistic anatomy, allows for an objective measure of performance and provides feedback to trainees.

Development of an optical fiber-based sensor for grasping and axial force sensing

In spite of the remarkable benefits that minimally invasive surgery provides for patients, the absence of force feedback is still a significant disadvantage. Several studies have been performed to address this issue; however, an accurate sterilizable force sensing technology for measuring axial and grasping forces is still missing. In this work, an innovative partial grasper has been designed and developed for a laparoscopic needle driver that can measure axial and grasping force information at the grasper tip. Fiber Bragg Grating sensors are used in this work because of their sterilizability and high sensitivity. Accuracies of 0.19 N and 0.26 N were achieved for the grasping and axial sensors respectively.