Current engineering developments for robotic systems in flexible endoscopy

The past four decades have seen an increase in the incidence of early-onset gastrointestinal cancer. Because early-stage cancer detection is vital to reduce mortality rate, mass screening colonoscopy provides the most effective prevention strategy. However, conventional endoscopy is a painful and technically challenging procedure that requires sedation and experienced endoscopists to be performed. To overcome the current limitations, technological innovation is needed in colonoscopy. In recent years, researchers worldwide have worked to enhance the diagnostic and therapeutic capabilities of endoscopes. The new frontier of endoscopic interventions is represented by robotic flexible endoscopy. Among all options, self-propelling soft endoscopes are particularly promising thanks to their dexterity and adaptability to the curvilinear gastrointestinal anatomy. For these devices to replace the standard endoscopes, integration with embedded sensors and advanced surgical navigation technologies must be investigated. In this review, the progress in robotic endoscopy was divided into the fundamental areas of design, sensing, and imaging. The article offers an overview of the most promising advancements on these three topics since 2018. Continuum endoscopes, capsule endoscopes, and add-on endoscopic devices were included, with a focus on fluid-driven, tendon-driven, and magnetic actuation. Sensing methods employed for the shape and force estimation of flexible endoscopes were classified into model- and sensor-based approaches. Finally, some key contributions in molecular imaging technologies, artificial neural networks, and software algorithms are described. Open challenges are discussed to outline a path toward clinical practice for the next generation of endoscopic devices

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

Seeing the Big Picture: System Architecture Trends in Endoscopy and LED-Based hyperspectral Subsystem Intergration

Early-stage colorectal lesions remain difficult to detect. Early development of neoplasia tends to be small (less than 10 mm) and flat and difficult to distinguish from surrounding mucosa. Additionally, optical diagnosis of neoplasia as benign or malignant is problematic. Low rates of detection of these lesions allow for continued growth in the colorectum and increased risk of cancer formation. Therefore, it is crucial to detect neoplasia and other non-neoplastic lesions to determine risk and guide future treatment. Technology for detection needs to enhance contrast of subtle tissue differences in the colorectum and track multiple biomarkers simultaneously. This work implements one such technology with the potential to achieve the desired multi-contrast outcome for endoscopic screenings: hyperspectral imaging. Traditional endoscopic imaging uses a white light source and a RGB detector to visualize the colorectum using reflected light. Hyperspectral imaging (HSI) acquires an image over a range of individual wavelength bands to create an image hypercube with a wavelength dimension much deeper and more sensitive than that of an RGB image. A hypercube can consist of reflectance or fluorescence (or both) spectra depending on the filtering optics involved. Prior studies using HSI in endoscopy have normally involved ex vivo tissues or xiv optics that created a trade-off between spatial resolution, spectral discrimination and temporal sampling. This dissertation describes the systems design of an alternative HSI endoscopic imaging technology that can provide high spatial resolution, high spectral distinction and video-rate acquisition in vivo. The hyperspectral endoscopic system consists of a novel spectral illumination source for image acquisition dependent on the fluorescence excitation (instead of emission). Therefore, this work represents a novel contribution to the field of endoscopy in combining excitation-scanning hyperspectral imaging and endoscopy. This dissertation describes: 1) systems architecture of the endoscopic system in review of previous iterations and theoretical next-generation options, 2) feasibility testing of a LED-based hyperspectral endoscope system and 3) another LED-based spectral illuminator on a microscope platform to test multi-spectral contrast imaging. The results of the architecture point towards an endoscopic system with more complex imaging and increased computational capabilities. The hyperspectral endoscope platform proved feasibility of a LED-based spectral light source with a multi-furcated solid light guide. Another LED-based design was tested successfully on a microscope platform with a dual mirror array similar to telescope designs. Both feasibility tests emphasized optimization of coupling optics and combining multiple diffuse light sources to a common output. These results should lead to enhanced imagery for endoscopic tissue discrimination and future optical diagnosis for routine colonoscopy

New Techniques in Gastrointestinal Endoscopy

As result of progress, endoscopy has became more complex, using more sophisticated devices and has claimed a special form. In this moment, the gastroenterologist performing endoscopy has to be an expert in macroscopic view of the lesions in the gut, with good skills for using standard endoscopes, with good experience in ultrasound (for performing endoscopic ultrasound), with pathology experience for confocal examination. It is compulsory to get experience and to have patience and attention for the follow-up of thousands of images transmitted during capsule endoscopy or to have knowledge in physics necessary for autofluorescence imaging endoscopy. Therefore, the idea of an endoscopist has changed. Examinations mentioned need a special formation, a superior level of instruction, accessible to those who have already gained enough experience in basic diagnostic endoscopy. This is the reason for what these new issues of endoscopy are presented in this book of New techniques in Gastrointestinal Endoscopy

Detection of Intestinal Bleeding in Wireless Capsule Endoscopy using Machine Learning Techniques

Gastrointestinal (GI) bleeding is very common in humans, which may lead to fatal consequences. GI bleeding can usually be identified using a flexible wired endoscope. In 2001, a newer diagnostic tool, wireless capsule endoscopy (WCE) was introduced. It is a swallow-able capsule-shaped device with a camera that captures thousands of color images and wirelessly sends those back to a data recorder. After that, the physicians analyze those images in order to identify any GI abnormalities. But it takes a longer screening time which may increase the danger of the patients in emergency cases. It is therefore necessary to use a real-time detection tool to identify bleeding in the GI tract. Each material has its own spectral ‘signature’ which shows distinct characteristics in specific wavelength of light [33]. Therefore, by evaluating the optical characteristics, the presence of blood can be detected. In the study, three main hardware designs were presented: one using a two-wavelength based optical sensor and others using two six-wavelength based spectral sensors with AS7262 and AS7263 chips respectively to determine the optical characteristics of the blood and non-blood samples. The goal of the research is to develop a machine learning model to differentiate blood samples (BS) and non-blood samples (NBS) by exploring their optical properties. In this experiment, 10 levels of crystallized bovine hemoglobin solutions were used as BS and 5 food colors (red, yellow, orange, tan and pink) with different concentrations totaling 25 non-blood samples were used as NBS. These blood and non-blood samples were also combined with pig’s intestine to mimic in-vivo experimental environment. The collected samples were completely separated into training and testing data. Different spectral features are analyzed to obtain the optical information about the samples. Based on the performance on the selected most significant features of the spectral wavelengths, k-nearest neighbors algorithm (k-NN) is finally chosen for the automated bleeding detection. The proposed k-NN classifier model has been able to distinguish the BS and NBS with an accuracy of 91.54% using two wavelengths features and around 89% using three combined wavelengths features in the visible and near-infrared spectral regions. The research also indicates that it is possible to deploy tiny optical detectors to detect GI bleeding in a WCE system which could eliminate the need of time-consuming image post-processing steps

Low-Cost Technologies for Flexible Endoscopy: Design, Control and Autonomy for a Water-Jet Actuated Soft Continuum Endoscope

Despite the outstanding diagnostic performance brought by new technologies in medicine, cancer remains a significant burden worldwide. In addition to prevention strategies, the ability to detect malignancy early is crucial in enabling effective treatment and dramatically increasing the survival rate of patients. In the case of gastric cancer, diagnosis is generally performed using Flexible Endoscopy (or Endoscope) (FE). The FE has been proven to be a powerful, reliable and cost-effective tool in the fight against gastric cancer. However, its effectiveness strongly depends on the skills of trained Gastro Enterologists (GE) who perform the procedures. Moreover, accessibility and availability of such tools are often limited to people residing in major cities, while remote and rural areas remain poorly served by their health systems. The advent of robotics in medicine offers a new solution to these problems. When possible, automating diagnostic procedures or surgical tasks has the potential to deliver reliable, repeatable and cost-effective alternatives to standard human-in-the-loop procedures. Embedding autonomous capabilities into a machine, optimally designed to execute a specific task, could enable the device to automatically adapt to different conditions and non-skilled personnel to perform the procedure by supervising the actions of the robotic platform. In these scenarios, safety represents a major concern and in the majority of the cases, a safe interaction between the robot and the tissues can be guaranteed by building compliant robots made of soft materials. However, if the possibility of using compliant devices offers a number of advantages to the final user or patient, it defines a series of technical challenges that have to be addressed to deliver a stable and reliable control of the platform. Finally, by adopting low-cost designs, single-use solutions can be realised to address the issue and complication of sterilisation. This dissertation discusses the research effort targeted at the development of a water-jet actuated low-cost, disposable gastroscopy platform to offer a safe, cost-effective, fault-free alternative to standard FE