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

GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}

Detection of Abnormality in Endoscopic Images using Endoscopic Technique

Medical imaging has been undergoing a revolution in the past decade with the advent of faster, more accurate and less invasive devices. This has driven the need for corresponding software development which in turn has provided a major impetus for new algorithms in signal and image processing. Digital image processing is important for many biomedical applications. The medical images analyzed, used as diagnostic tools and quite often provide insight into the inner working of the process under study. The commonly found abnormalities in endoscopic images are cancer tumors, ulcers, bleeding due to internal injuries, etc. The segmented method is used to segment the tumor, abnormal regions and cancerous growth in the human esophagus. In our proposed work, a method for detecting possible presence of abnormality in the endoscopic images is presented. An algorithm is to develop to perform the segmentation, classification and analysis of medical images, especially the endoscopic images for the identification of commonly occurring abnormalities in it

Building up the Future of Colonoscopy – A Synergy between Clinicians and Computer Scientists

Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly

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

Computer Image Analysis Based Quantification of Comparative Ihc Levels of P53 And Signaling Associated With the Dna Damage Repair Pathway Discriminates Between Inflammatory And Dysplastic Cellular Atypia

Epithelial oncogenesis is believed to be generally associated with the accumulation over time of an increasing number of mitotic errors until a threshold number of mutations required for the initiation of cancer is achieved. Preemption of cancer through the morphologic detection of dysplastic cells, i.e. cells with a number of mitotic errors that are still below the threshold for cancer, followed by their surgical removal or eradication, has had an enormous impact on reducing the incidence of cancer of the uterine cervix, skin and colon worldwide, but this strategy has been much less successful with cancers in most other body sites. Inflammation is a relatively common occurrence in the epithelium and is far more common than cancer. A major current obstacle to the preemption of carcinoma is distinguishing morphologically atypical epithelial cells in the presence of inflammation (inflammatory atypia) that mimic dysplasia from morphologically atypical epithelial cells that are truly dysplastic. Formation of double stranded breaks in DNA (DSBs) is an accepted etiology for carcinoma and is, therefore, expected to be associated with dysplasia. Utilizing both algorithmic and artificial intelligence-based computer image analysis of IHC levels, we document the unexpected finding that phosphorylation of molecular markers associated with DSBs is consistently correlated with non-dysplastic iv inflammatory atypia in both squamous (oral cavity) and glandular (Barrett’s metaplasia) epithelia. Using these same image analysis methods, we further show that quantitative immunohistochemistry of the ratio of p-Chk2, a marker of DSB’s, and for mutational failure of the DNA damage repair pathway (p53) required for the proper response to DSBs can distinguish between inflammatory and dysplastic cellular atypia. The ability to use quantitative means to reliably distinguish between inflammatory and dysplastic atypia may facilitate the use of cytological screening for dysplasia to prevent cancer in numerous body sites

A Systematic Survey of Classification Algorithms for Cancer Detection

Cancer is a fatal disease induced by the occurrence of a count of inherited issues and also a count of pathological changes. Malignant cells are dangerous abnormal areas that could develop in any part of the human body, posing a life-threatening threat. To establish what treatment options are available, cancer, also referred as a tumor, should be detected early and precisely. The classification of images for cancer diagnosis is a complex mechanism that is influenced by a diverse of parameters. In recent years, artificial vision frameworks have focused attention on the classification of images as a key problem. Most people currently rely on hand-made features to demonstrate an image in a specific manner. Learning classifiers such as random forest and decision tree were used to determine a final judgment. When there are a vast number of images to consider, the difficulty occurs. Hence, in this paper, weanalyze, review, categorize, and discuss current breakthroughs in cancer detection utilizing machine learning techniques for image recognition and classification. We have reviewed the machine learning approaches like logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), decision tree (DT), and Support Vector Machines (SVM)