A Review of Locomotion Systems for Capsule Endoscopy

Wireless capsule endoscopy for gastrointestinal (GI) tract is a modern technology that has the potential to replace conventional endoscopy techniques. Capsule endoscopy is a pill-shaped device embedded with a camera, a coin battery, and a data transfer. Without a locomotion system, this capsule endoscopy can only passively travel inside the GI tract via natural peristalsis, thus causing several disadvantages such as inability to control and stop, and risk of capsule retention. Therefore, a locomotion system needs to be added to optimize the current capsule endoscopy. This review summarizes the state-of-the-art locomotion methods along with the desired locomotion features such as size, speed, power, and temperature and compares the properties of different methods. In addition, properties and motility mechanisms of the GI tract are described. The main purpose of this review is to understand the features of GI tract and diverse locomotion methods in order to create a future capsule endoscopy compatible with GI tract properties

A survey of small bowel modelling and its applications for capsule endoscopy

This is the final version. Available on open access from Elsevier via the DOI in this recordThe small intestine, an anatomical site previously considered inaccessible to clinicians due to its small diameter and length, is the part of the gastrointestinal tract between the stomach and the colon. Since its introduction into clinical practice two decades ago, capsule endoscopy has become established as the primary modality for examining the surface lining of the small intestine. Today, researchers continue to develop ground-breaking technologies for novel miniature devices aiming for tissue biopsy, drug delivery and therapy. The purpose of this paper is to provide researchers and engineers in this area a comprehensive review of the progress in understanding the anatomy and physiology of the small intestine and how this understanding was translated to virtual and physical test platforms for assessing the performance of these intestinal devices. This review will cover both theoretical and practical studies on intestinal motor activities and the work on mathematical modelling and experimental investigation of capsule endoscope in the small intestine. In the end, the requirements for improving the current work are drawn, and the expectations on future research in this field are provided.Engineering and Physical Sciences Research Council (EPSRC)China Scholarship Counci

Frontiers of robotic endoscopic capsules: a review

Digestive diseases are a major burden for society and healthcare systems, and with an aging population, the importance of their effective management will become critical. Healthcare systems worldwide already struggle to insure quality and affordability of healthcare delivery and this will be a significant challenge in the midterm future. Wireless capsule endoscopy (WCE), introduced in 2000 by Given Imaging Ltd., is an example of disruptive technology and represents an attractive alternative to traditional diagnostic techniques. WCE overcomes conventional endoscopy enabling inspection of the digestive system without discomfort or the need for sedation. Thus, it has the advantage of encouraging patients to undergo gastrointestinal (GI) tract examinations and of facilitating mass screening programmes. With the integration of further capabilities based on microrobotics, e.g. active locomotion and embedded therapeutic modules, WCE could become the key-technology for GI diagnosis and treatment. This review presents a research update on WCE and describes the state-of-the-art of current endoscopic devices with a focus on research-oriented robotic capsule endoscopes enabled by microsystem technologies. The article also presents a visionary perspective on WCE potential for screening, diagnostic and therapeutic endoscopic procedures

New Robotic Technologies in Cancer Colon Screening

Colorectal cancer (CRC) is the 3rd most common cause of cancer death worldwide. Regular screening of the asymptomatic population can drastically reduce the mortality rate. CRC screening includes several proceedings although the gold standard remains optical colonoscopy (OC), which is unpleasant, causes pain and discomfort. New technologies exemplified by capsule endoscopy (CE) constitute alternative painless solutions and despite their limitations, e.g., passive locomotion and absence of on-board instrumentation, are being increasingly used for CRC screening. Research and development centres are investigating novel advanced robotic technologies for diagnostic and therapeutic use. These include wireless communication, active locomotion, sensors, diagnostic, and therapeutic instruments. This review describes the traditional OC procedure and the existing robotic technologies for CRC

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

Magnetically Assisted Capsule Endoscopy: A Viable Alternative to Conventional Flexible Endoscopy of the Stomach?

INTRODUCTION: Oesophagogastroduodenoscopy is the investigation of choice to identify mucosal lesions of the upper gastrointestinal tract, but it is poorly tolerated by patients. A simple non-invasive technique to image the upper gastrointestinal tract, which could be made widely available, would be beneficial to patients. Capsule endoscopy is well tolerated by patients but the stomach has proved difficult to visualise accurately with capsule technology due to its’ capacious nature and mucosal folds, which can obscure pathology. MiroCam Navi (Intromedic Ltd, Seoul, Korea) is a capsule endoscope containing a small amount of magnetic material which has been made available with a handheld magnet which might allow a degree of control. This body of work aims to address whether this new technology could be a feasible alternative to conventional flexible endoscopy of the stomach. METHODS: Four studies were conducted to test this research question. The first explores the feasibility of magnetically assisted capsule endoscopy of the stomach and operator learning curve in an ex vivo porcine model. This was followed by a randomised, blinded trial comparing magnetically assisted capsule endoscopy to conventional flexible endoscopy in ex vivo porcine stomach models. Subsequently a prospective, single centre randomised controlled trial in humans examined whether magnetically assisted capsule endoscopy could enhance conventional small bowel capsule endoscopy by reducing gastric transit time. Finally a blinded comparison of diagnostic yield of magnetically assisted capsule endoscopy compared to oesophagogastroduodenoscopy was performed in patients with recurrent or refractory iron deficiency anaemia. RESULTS: In the first study all stomach tags were identified in 87.2% of examinations and a learning curve was demonstrated (mean examination times for the first 23 and second 23 procedures 10.28 and 6.26 minutes respectively (p<0.001). In the second study the difference in sensitivities between oesophagogastroduodenoscopy and conventional flexible endoscopy for detecting beads within an ex vivo porcine stomach model was 1.11 (95% CI 0.06, 28.26) proving magnetically assisted capsule endoscopy to be non-inferior to flexible endoscopy. In the first human study, although there was no significant difference in gastric transit time or capsule endoscopy completion rate between the two groups (p=0.12 and p=0.39 respectively), the time to first pyloric image was significantly shorter in the intervention group (p=0.03) suggesting that magnetic control hastens capsular transit to the gastric antrum but cannot impact upon duodenal passage. In the last study, a total of 38 pathological findings were identified in this comparative study of magnetically assisted capsule endoscopy and conventional endoscopy. Of these, 16 were detected at both procedures, while flexible endoscopy identified 14 additional lesions not seen at magnetically assisted capsule endoscopy and magnetically assisted capsule endoscopy detected 8 abnormalities not seen by oesophagogastroduodenoscopy. No adverse events occurred in either of the human trials. Finally magnetically steerable capsule endoscopy induced less procedural pain, discomfort and distress than oesophagogastroduodenoscopy (p=0.0009, p=0.001 and p=0.006 respectively). CONCLUSION: Magnetically assisted capsule endoscopy is safe, well tolerated and a viable alternative to conventional endoscopy. Further research to develop and improve this new procedure is recommended

Anatomical Classification of the Gastrointestinal Tract Using Ensemble Transfer Learning

Endoscopy is a procedure used to visualize disorders of the gastrointestinal (GI) lumen. GI disorders can occur without symptoms, which is why gastroenterologists often recommend routine examinations of the GI tract. It allows a doctor to directly visualize the inside of the GI tract and identify the cause of symptoms, reducing the need for exploratory surgery or other invasive procedures. It can also detect the early stages of GI disorders, such as cancer, enabling prompt treatment that can improve outcomes. Endoscopic examinations generate significant numbers of GI images. Because of this vast amount of endoscopic image data, relying solely on human interpretation can be problematic. Artificial intelligence is gaining popularity in clinical medicine. Assist in medical image analysis and early detection of diseases, help with personalized treatment planning by analyzing a patient’s medical history and genomic data, and be used by surgical robots to improve precision and reduce invasiveness. It enables automated diagnosis, provides physicians with assistance, and may improve performance. One of the significant challenges is defining the specific anatomic locations of GI tract abnormalities. Clinicians can then determine appropriate treatment options, reducing the need for repetitive endoscopy. Due to the difficulty of collecting annotated data, very limited research has been conducted on the localization of anatomical locations by classification of endoscopy images. In this study, we present a classification of GI tract anatomical localization based on transfer learning and ensemble learning. Our approach involves the use of an autoencoder and the Xception model. The autoencoder was initially trained on thousands of unlabeled images, and the encoder then separated and used as a feature extractor. The Xception model was also used as a second model to extract features from the input images. The extracted feature vectors were then concatenated and fed into a Convolutional Neural Network for classification. This combination of models provides a powerful and versatile solution for image classification. By using the encoder as a feature extractor that can transfer the learned knowledge, it is possible to improve learning by allowing the model to focus on more relevant and useful data, which is extremely valuable when there are not enough appropriately labelled data. On the other hand, the Xception model provides additional feature extraction capabilities. Sometimes, one classifier is not enough in machine learning, as it depends on the problem we are trying to solve and the quality and quantity of data available. With ensemble learning, multiple learning networks can work together to create a stronger classifier. The final classification results are obtained by combining the information from both models through the CNN model. This approach demonstrates the potential for combining multiple models to improve the accuracy of image classification tasks in the medical domain. The HyperKvasir dataset is the main dataset used in this study. It contains 4,104 labelled and 99,417 unlabeled images taken at six different locations in the GI tract, including the cecum, ileum, pylorus, rectum, stomach, and Z line. After dataset preprocessing, which includes noise deduction and similarity removal, 871 labelled images remained for the purpose of this study. Our method was more accurate than state-of-the-art studies and had a higher F1 score while categorizing the input images into six different anatomical locations with less than a thousand labelled images. According to the results, feature extraction and ensemble learning increase accuracy by 5%, and a comparison with existing methods using the same dataset indicate improved performance and reduced cross entropy loss. The proposed method can therefore be used in the classification of endoscopy images

Processamento de imagens médicas usando GPU

Mestrado em Engenharia de Computadores e TelemáticaA aplicação CapView utiliza um algoritmo de classificação baseado em SVM (Support Vector Machines) para automatizar a segmentação topográfica de vídeos do trato intestinal obtidos por cápsula endoscópica. Este trabalho explora a aplicação de processadores gráficos (GPU) para execução paralela desse algoritmo. Após uma etapa de otimização da versão sequencial, comparou-se o desempenho obtido por duas abordagens: (1) desenvolvimento apenas do código do lado do host, com suporte em bibliotecas especializadas para a GPU, e (2) desenvolvimento de todo o código, incluindo o que é executado no GPU. Ambas permitiram ganhos (speedups) significativos, entre 1,4 e 7 em testes efetuados com GPUs individuais de vários modelos. Usando um cluster de 4 GPU do modelo de maior capacidade, conseguiu-se, em todos os casos testados, ganhos entre 26,2 e 27,2 em relação à versão sequencial otimizada. Os métodos desenvolvidos foram integrados na aplicação CapView, utilizada em rotina em ambientes hospitalares.The CapView application uses a classification algorithm based on SVMs (Support Vector Machines) for automatic topographic segmentation of gastrointestinal tract videos obtained through capsule endoscopy. This work explores the use graphic processors (GPUs) to parallelize the segmentation algorithm. After an optimization phase of the sequential version, two new approaches were analyzed: (1) development of the host code only, with support of specialized libraries for the GPU, and (2) development of the host and the device’s code. The two approaches caused substantial gains, with speedups between 1.4 and 7 times in tests made with several different individual GPUs. In a cluster of 4 GPUs of the most capable model, speedups between 26.2 and 27.2 times were achieved, compared to the optimized sequential version. The methods developed were integrated in the CapView application, used in routine in medical environments