SmartProbe: a bioimpedance sensing system for head and neck cancer tissue detection

Development and evaluation of a robotic system for lumbar puncture and epidural steroid injection

Introduction

Lumbar puncture is an important medical procedure for various diagnostics and therapies, but it can be hazardous due to individual variances in subcutaneous soft tissue, especially in the elderly and obese. Our research describes a novel robot-assisted puncture system that automatically controls and maintains the probe at the target tissue layer through a process of tissue recognition.

Methods

The system comprises a robotic system and a master computer. The robotic system is constructed based on a probe consisting of a pair of concentric electrodes. From the probe, impedance spectroscopy measures bio-impedance signals and transforms them into spectra that are communicated to the master computer. The master computer uses a Bayesian neural network to classify the bio-impedance spectra as corresponding to different soft tissues. By feeding the bio-impedance spectra of unknown tissues into the Bayesian neural network, we can determine their categories. Based on the recognition results, the master computer controls the motion of the robotic system.

Results

The proposed system is demonstrated on a realistic phantom made of ex vivo tissues to simulate the spinal environment. The findings indicate that the technology has the potential to increase the precision and security of lumbar punctures and associated procedures.

Discussion

In addition to lumbar puncture, the robotic system is suitable for related puncture operations such as discography, radiofrequency ablation, facet joint injection, and epidural steroid injection, as long as the required tissue recognition features are available. These operations can only be carried out once the puncture needle and additional instruments reach the target tissue layer, despite their ensuing processes being distinct.

Electric Bioimpedance Sensing for the Detection of Head and Neck Squamous Cell Carcinoma

The early detection of head and neck squamous cell carcinoma (HNSCC) is essential to improve patient prognosis and enable organ and function preservation treatments. The objective of this study is to assess the feasibility of using electrical bioimpedance (EBI) sensing technology to detect HNSCC tissue. A prospective study was carried out analyzing tissue from 46 patients undergoing surgery for HNSCC. The goal was the correct identification of pathologic tissue using a novel needle-based EBI sensing device and AI-based classifiers. Considering the data from the overall patient cohort, the system achieved accuracies between 0.67 and 0.93 when tested on tissues from the mucosa, skin, muscle, lymph node, and cartilage. Furthermore, when considering a patient-specific setting, the accuracy range increased to values between 0.82 and 0.95. This indicates that more reliable results may be achieved when considering a tissue-specific and patient-specific tissue assessment approach. Overall, this study shows that EBI sensing may be a reliable technology to distinguish pathologic from healthy tissue in the head and neck region. This observation supports the continuation of this research on the clinical use of EBI-based devices for early detection and margin assessment of HNSCC.

Design and Manufacturing of Equipment for Investigation of Low Frequency Bioimpedance

The purpose of this study was to highlight a method of making equipment for the investigation of low frequency bioimpedance. A constant current with an average value of I = 100 µA is injected into the human body via means of current injection electrodes, and the biological signal is taken from the electrodes of electric potential charged with the biopotentials generated by the human body. The resulting voltage, ΔU is processed by the electronic conditioning system. The mathematical model of the four-electrode system in contact with the skin, and considering a target organ, was simplified to a single equivalent impedance. The capacitive filter low passes down from the differential input of the first instrumentation amplifier together with the isolated capacitive barrier integrated in the precision isolated secondary amplifier and maintains the biological signal taken from the electrodes charged with the undistorted biopotentials generated by the human body. Mass loops are avoided, and any electric shocks or electrostatic discharges are prevented. In addition, for small amplitudes of the biological signal, electromagnetic interferences of below 100 Hz of the power supply network were eliminated by using an active fourth-order Bessel filtering module. The measurements performed for the low frequency of f = 100 Hz on the volunteers showed for the investigated organs that the bioelectrical resistivities vary from 90 Ωcm up to 450 Ωcm, and that these are in agreement with other published and disseminated results for each body zone.

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

OBJECTIVE

One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus.

METHODS

Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10-100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0-25.0°C and 50%-60% humidity. Two types of tissue recognition models - one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning - were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two-way ANOVA, and paired T-test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies.

RESULTS

The results suggest that the differences of electrical impedance mainly came from tissue type (p < 0.0001), the electrical impedance of five kinds of tissue was statistically different from each other (p < 0.0001). The tissue recognition accuracy of the algorithm based on principal component analysis and support vector machine ranged from 83%-100%, and the overall accuracy was 95.83%. The classification accuracy of the algorithm based on support vector machine and ensemble learning was 100%, and the boundaries of electrical impedance of five tissues at various frequencies were calculated.

CONCLUSION

The electrical impedance of cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus had significant differences in 10-100 kHz frequency. The application of support vector machine realized the accurate tissue recognition in the spinal operation area based on electrical impedance, which is expected to be translated and applied to tissue recognition during spinal surgery.

Monopolar, bipolar, tripolar, and tetrapolar configurations in robot assisted electrical impedance scanning

OBJECTIVE

Tissue recognition is a critical process during a Robot-assisted minimally invasive surgery (RMIS) and it relies on the involvement of advanced sensing technology.

APPROACH

In this paper, the concept of Robot Assisted Electrical Impedance Sensing (RAEIS) is utilized and further developed aiming to sense the electrical bioimpedance of target tissue directly based on the existing robotic instruments and control strategy. Specifically, we present a new sensing configuration called pseudo-tetrapolar method. With the help of robotic control, we can achieve a similar configuration as traditional tetrapolar, and with better accuracy.

MAIN RESULTS

Five configurations including monopolar, bipolar, tripolar, tetrapolar and pseudo-tetrapolar are analyzed and compared through simulation experiments. Advantages and disadvantages of each configuration are thus discussed.

SIGNIFICANCE

This study investigates the measurement of tissue electrical property directly based on the existing robotic surgical instruments. Specifically, different sensing configurations can be realized through different connection and control strategies, making them suitable for different application scenarios.

Humanin vivoliver and tumor bioimpedance measured with biopsy needle

Objective:Liver biopsy is an essential procedure in cancer diagnostics but targeting the biopsy to the actual tumor tissue is challenging. Aim of this study was to evaluate the clinical feasibility of a novel bioimpedance biopsy needle system in liver biopsy and simultaneously to gatherin vivobioimpedance data from human liver and tumor tissues.Approach:We measured human liver and tumor impedance datain vivofrom 26 patients who underwent diagnostic ultrasound-guided liver biopsy. Our novel 18G core biopsy needle tip forms a bipolar electrode that was used to measure bioimpedance during the biopsy in real-time with frequencies from 1 kHz to 349 kHz. The needle tip location was determined by ultrasound. Also, the sampled tissue type was determined histologically.Main results:The bioimpedance values showed substantial variation between individual cases, and liver and tumor data overlapped each other. However, Mann-Whitney U test showed that the median bioimpedance values of liver and tumor tissue are significantly (p<0.05) different concerning the impedance magnitude at frequencies below 25 kHz and the phase angle at frequencies below 3 kHz and above 30 kHz.Significance:This study uniquely employed a real-time bioimpedance biopsy needle in clinical liver biopsies and reported the measured humanin vivoliver and tumor impedance data. Impedance is always device-dependent and therefore not directly comparable to measurements with other devices. Although the variation in tumor types prevented coherent tumor identification, our study provides preliminary evidence that tumor tissue differs from liver tissuein vivoand this association is frequency-dependent.

A Multi-Frequency Focused Impedance Measurement System Based on Analogue Synchronous Peak Detection

Monitoring of anatomical structures and physiological processes by electrical impedance has attracted scientists as it is noninvasive, nonionizing and the instrumentation is relatively simple. Focused Impedance Method (FIM) is attractive in this context, as it has enhanced sensitivity at the central region directly beneath the electrode configuration minimizing contribution from neighboring regions. FIM essentially adds or averages two concentric and orthogonal combinations of conventional Tetrapolar Impedance Measurements (TPIM) and has three versions with 4, 6, and 8 electrodes. This paper describes the design and testing of a multi-frequency FIM (MFFIM) system capable of measuring all three versions of FIM at 8 frequencies in the range 10 kHz—1 MHz. A microcontroller based multi-frequency signal generator and a balanced Howland current source with high output impedance (476 kΩ at 10 kHz and 58.3 kΩ at 1 MHz) were implemented for driving currents into biological tissues with an error &lt;1%. The measurements were carried out at each frequency sequentially. The peak values of the amplified voltage signals were measured using a novel analogue synchronous peak detection technique from which the transfer impedances were obtained. The developed system was tested using TPIM measurements on a passive RC Cole network placed between two RC networks, the latter representing skin-electrode contact impedances. Overall accuracy of the measurement was very good (error &lt;4% at all frequencies except 1 MHz, with error 6%) and the resolution was 0.1 Ω. The designed MFFIM system had a sampling rate of &gt;45 frames per second which was deemed adequate for noninvasive real-time impedance measurements on biological tissues.

Smart handheld device with flexible wrist and electrical bioimpedance sensor for tissue inspection

With the evolving demands of surgical intervention, there is a strong need for smaller and functionally augmented instruments to improve surgical outcomes, operational convenience, and diagnostic safety. Owing to the narrow and complicated anatomy, the probe head of the medical instrument is required to possess both good maneuverability and compact size. In addition, the development of medical instrument is moving toward patient-specialized, of which the articulation positions can be customized to reach the target position. To fulfill these requirements, this study presents the design of a smart handheld device which equips with a low cost, easy control, disposable flexible wrist, and an electrical bioimpedance sensor for medical diagnosis. Prototype of the device is made and tested. The experimental results demonstrate that the proposed device can provide accurate manipulation and effective tissue detection, showing a great potential in various medical applications.

Robot-Assisted Electrical Impedance Scanning system for 2D Electrical Impedance Tomography tissue inspection

The electrical impedance tomography (EIT) technology is an important medical imaging approach to show the electrical characteristics and the homogeneity of a tissue region noninvasively. Recently, this technology has been introduced to the Robot Assisted Minimally Invasive Surgery (RAMIS) for assisting the detection of surgical margin with relevant clinical benefits. Nevertheless, most EIT technologies are based on a fixed multiple-electrodes probe which limits the sensing flexibility and capability significantly. In this study, we present a method for acquiring the EIT measurements during a RAMIS procedure using two already existing robotic forceps as electrodes. The robot controls the forceps tips to a series of predefined positions for injecting excitation current and measuring electric potentials. Given the relative positions of electrodes and the measured electric potentials, the spatial distribution of electrical conductivity in a section view can be reconstructed. Realistic experiments are designed and conducted to simulate two tasks: subsurface abnormal tissue detection and surgical margin localization. According to the reconstructed images, the system is demonstrated to display the location of the abnormal tissue and the contrast of the tissues' conductivity with an accuracy suitable for clinical applications.

An electrical bioimpedance scanning system for subsurface tissue detection in Robot Assisted Minimally Invasive Surgery

In Robot Assisted Minimally Invasive Surgery, discriminating critical subsurface structures is essential to make the surgical procedure safer and more efficient. In this paper, a novel robot assisted electrical bio-impedance scanning (RAEIS) system is developed and validated using a series of experiments. The proposed system constructs a tri-polar sensing configuration for tissue homogeneity inspection. Specifically, two robotic forceps are used as electrodes for applying electric current and measuring reciprocal voltages relative to a ground electrode which is placed distal from the measuring site. Compared to the other existing electrical bioimpedance sensing technology, the proposed system is able to use miniaturized electrodes to measure a site flexibly with enhanced subsurfacial detection capability. In this paper, we present the concept, the modeling of the sensing method, the hardware design, and the system calibration. Subsequently, a series of experiments are conducted for system evaluation including finite element simulation, saline solution bath experiments and experiments based on ex vivo animal tissues. The experimental results demonstrate that the proposed system can measure the resistivity of the material with high accuracy, and detect a subsurface non-homogeneous object with 100% success rate. The proposed parameters estimation algorithm is able to approximate the resistivity and the depth of the subsurface object effectively with one fast scanning.

Design of a Drop-in EBI Sensor Probe for Abnormal Tissue Detection in Minimally Invasive Surgery

It is a common challenge for the surgeon to detect pathological tissues and determine the resection margin during a minimally invasive surgery. In this study, we present a drop-in sensor probe based on the electrical bioimpedance spectroscopic technology, which can be grasped by a laparoscopic forceps and controlled by the surgeon to inspect suspicious tissue area conveniently. The probe is designed with an optimized electrode and a suitable shape specifically for Minimally Invasive Surgery (MIS). Subsequently, a series of ex vivo experiments are carried out with porcine liver tissue for feasibility validation. During the experiments, impedance measured at frequencies from 1 kHz to 2 MHz are collected on both normal tissues and water soaked tissue. In addition, classifiers based on discriminant analysis are developed. The result of the experiment indicate that the sensor probe can be used to measure the impedance of the tissue easily and the developed tissue classifier achieved accuracy of 80% and 100% respectively.