Design and Integration of Electrical Bio-impedance Sensing in Surgical Robotic Tools for Tissue Identification and Display

Revolutionising Breast Surgery: A Comprehensive Review of Robotic Innovations in Breast Surgery and Reconstruction

Robotic innovations in breast surgery have ushered in a new era of precision, safety, and patient-centred care. This comprehensive review explores the multifaceted realm of robotic breast surgery, from preoperative planning to postoperative outcomes, learning curves for surgeons, and the implications for healthcare policies. We examine the ethical considerations, cost-effectiveness, and future directions, including integrating artificial intelligence and telesurgery. Key findings reveal that robotic systems provide improved surgical precision, reduced complications, and enhanced patient satisfaction. Ethical concerns encompass informed consent, resource allocation, and equitable access. The future of breast surgery lies in continued research and development, ensuring that robotics becomes a standard of care accessible to all patients. This technology is reshaping breast surgery and offering new possibilities for minimally invasive, patient-centred care, ultimately redefining the standards of care in this critical field of medicine.

Piezoresistivity modeling of soft tissue electrical-mechanical properties: A validation study

In general, the electrical property of soft tissues is sensitive to the force applied to their surface. To further study the relationship between the force and the electrical property of soft tissues, this paper attempts to investigate the effect of static and higher-order stresses on electrical properties. Overall, a practical experimental platform is designed to acquire the force information and the electrical property of soft tissues during a contact procedure, which is featured different compression stimuli, such as constant pressing force, constant pressing speed, and step-force compression, etc. Furthermore, the piezoresistive characteristic is innovatively introduced to model the mechanical-electrical properties of soft tissue. Finite Element Modeling (FEM) is adopted to fit the static piezoresistivity of the soft tissue. Finally, experimental studies were performed to demonstrate the effect of stress on the electrical properties and the feasibility of the proposed piezoresistive model to describe soft tissues' mechanical and electrical properties.

The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

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.

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.

Electrical Impedance Characterization of in Vivo Porcine Tissue Using Machine Learning

The incorporation of sensors onto the stapling platform has been investigated to overcome the disconnect in our understanding of tissue handling by surgical staplers. The goal of this study was to explore the feasibility of in vivo porcine tissue differentiation using bioimpedance data and machine learning methods. In vivo electrical impedance measurements were obtained in 7 young domestic pigs, using a logarithmic sweep of 50 points over a frequency range of 100 Hz to 1 MHz. Tissues studied included lung, liver, small bowel, colon, and stomach, which was further segmented into fundus, body, and antrum. The data was then parsed through MATLAB's classification learner to identify the best algorithm for tissue type differentiation. The most effective classification scheme was found to be cubic support vector machines with 86.96% accuracy. When fundus, body and antrum were aggregated together as stomach, the accuracy improved to 88.03%. The combination of stomach, small bowel, and colon together as GI tract improved accuracy to 99.79% using fine k nearest neighbors. The results suggest that bioimpedance data can be effectively used to differentiate tissue types in vivo. This study is one of the first that combines in vivo bioimpedance tissue data across multiple tissue types with machine learning methods.

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

OBJECTIVES

This study presents SmartProbe, an electrical bioimpedance (EBI) sensing system based on a concentric needle electrode (CNE). The system allows the use of commercial CNEs for accurate EBI measurement, and was specially developed for in-vivo real-time cancer detection.

APPROACH

Considering the uncertainties in EBI measurements due to the CNE manufacturing tolerances, we propose a calibration method based on statistical learning. This is done by extracting the correlation between the measured impedance value |Z|, and the material conductivity σ, for a group of reference materials. By utilizing this correlation, the relationship of σ and |Z| can be described as a function and reconstructed using a single measurement on a reference material of known conductivity.

MAIN RESULTS

This method simplifies the calibration process, and is verified experimentally. Its effectiveness is demonstrate by results that show less than 6% relative error. An additional experiment is conducted for evaluating the system's capability to detect cancerous tissue. Four types of ex-vivo human tissue from the head and neck region, including mucosa, muscle, cartilage and salivary gland, are characterized using SmartProbe. The measurements include both cancer and surrounding healthy tissue excised from 10 different patients operated on for head and neck cancer. The measured data is then processed using dimension reduction and analyzed for tissue classification. The final results show significant differences between pathologic and healthy tissues in muscle, mucosa and cartilage specimens.

SIGNIFICANCE

These results are highly promising and indicate a great potential for SmartProbe to be used in various cancer detection tasks.

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.