A comparison of artificial intelligence algorithms in diagnosing and predicting gastric cancer: a review study

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnose and predict diseases. Gastric cancer has been the fourth most common malignancy and the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity.  This narrative-review paper aims to explore AI algorithms in diagnosing and predicting gastric cancer. To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases. According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM (support vector machine), convolutional neural network (CNN), and deep-type CNN have been used the most; therefore, we propose the usage of these algorithms in medical subjects, especially in gastric cancer

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

Automatic Esophageal Abnormality Detection and Classification

Esophageal cancer is counted as one of the deadliest cancers worldwide ranking the sixth among all types of cancers. Early esophageal cancer typically causes no symp- toms and mainly arises from overlooked/untreated premalignant abnormalities in the esophagus tube. Endoscopy is the main tool used for the detection of abnormalities, and the cell deformation stage is confirmed by taking biopsy samples. The process of detection and classification is considered challenging for several reasons such as; different types of abnormalities (including early cancer stages) can be located ran- domly throughout the esophagus tube, abnormal regions can have various sizes and appearances which makes it difficult to capture, and failure in discriminating between the columnar mucosa from the metaplastic epithelium. Although many studies have been conducted, it remains a challenging task and improving the accuracy of auto- matically classifying and detecting different esophageal abnormalities is an ongoing field. This thesis aims to develop novel automated methods for the detection and classification of the abnormal esophageal regions (precancerous and cancerous) from endoscopic images and videos. In this thesis, firstly, the abnormality stage of the esophageal cell deformation is clas- sified from confocal laser endomicroscopy (CLE) images. The CLE is an endoscopic tool that provides a digital pathology view of the esophagus cells. The classifica- tion is achieved by enhancing the internal features of the CLE image, using a novel enhancement filter that utilizes fractional integration and differentiation. Different imaging features including, Multi-Scale pyramid rotation LBP (MP-RLBP), gray level co-occurrence matrices (GLCM), fractal analysis, fuzzy LBP and maximally stable extremal regions (MSER), are calculated from the enhanced image to assure a robust classification result. The support vector machine (SVM) and random forest (RF) classifiers are employed to classify each image into its pathology stage. Secondly, we propose an automatic detection method to locate abnormality regions from high definition white light (HD-WLE) endoscopic images. We first investigate the performance of different deep learning detection methods on our dataset. Then we propose an approach that combines hand-designed Gabor features with extracted convolutional neural network features that are used by the Faster R-CNN to detect abnormal regions. Moreover, to further improve the detection performance, we pro- pose a novel two-input network named GFD-Faster RCNN. The proposed method generates a Gabor fractal image from the original endoscopic image using Gabor filters. Then features are learned separately from the endoscopic image and the gen- erated Gabor fractal image using the densely connected convolutional network to detect abnormal esophageal regions. Thirdly, we present a novel model to detect the abnormal regions from endoscopic videos. We design a 3D Sequential DenseConvLstm network to extract spatiotem- poral features from the input videos that are utilized by a region proposal network and ROI pooling layer to detect abnormality regions in each frame throughout the video. Additionally, we suggest an FS-CRF post-processing method that incorpor- ates the Conditional Random Field (CRF) on a frame-based level to recover missed abnormal regions in neighborhood frames within the same clip. The methods are evaluated on four datasets: (1) CLE dataset used for the classific- ation model, (2) Publicly available dataset named Kvasir, (3) MICCAI’15 Endovis challenge dataset, Both datasets (2) and (3) are used for the evaluation of detection model from endoscopic images. Finally, (4) Gastrointestinal Atlas dataset used for the evaluation of the video detection model. The experimental results demonstrate promising results of the different models and have outperformed the state-of-the-art methods

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Aquisição, tratamento, arquivo e difusão de exames de endoscopia

Dissertação de mestrado integrado em Engenharia BiomédicaDe entre os diversos tipos de exames de endoscopia, a esofagogastroduodenoscopia assume um papel preponderante devido a ser o método ideal para examinar a mucosa do trato digestivo alto, bem como para detetar inúmeras patologias gastrenterológicas. O resultado deste tipo de exames é, geralmente, um relatório composto por um conjunto de frames capturados durante o exame, eventualmente acompanhado por um vídeo. Hoje em dia, apenas as imagens juntamente com o relatório endoscópico, são arquivadas. O facto de o vídeo não ser arquivado pode conduzir a um incómodo no bem-estar do paciente, assim como a um acréscimo de custos e tempo despendido, pois frequentemente o mesmo é necessário para revisão e validação da hipótese de diagnóstico, bem como para comparação de segmentos do vídeo com exames futuros. Mesmo nos casos em que a informação é arquivada, a falta de reutilização e partilha de informação e vídeos entre entidades contribui, mais uma vez, para uma repetição desnecessária de exames. A existência de um arquivo de vídeos endoscópicos seria uma mais-valia, pois além de resolver os problemas referidos ainda possibilitaria a sua utilização para fins de pesquisa e investigação, além de disponibilizar exames para servirem como referência para estudo de casos similares. Neste trabalho é proposta uma solução abrangente para a aquisição, tratamento, arquivo e difusão de exames de endoscopia. O objetivo passa por disponibilizar um sistema capaz de gerir toda a informação clínica e administrativa (incluindo conteúdo audiovisual) desde o seu processo de aquisição até ao processo de pesquisa de exames antigos, para comparação com novos casos. De forma a garantir a compatibilidade lexical da informação partilhada no sistema, foi utilizado um vocabulário endoscópico estandardizado, o Minimal Standard Terminology (MST). Neste contexto foi planeado um dispositivo (MIVbox) orientado à aquisição do vídeo endoscópico, independentemente da câmara endoscópica utilizada. Toda a informação é armazenada de forma estruturada e normalizada, possibilitando a sua reutilização e difusão. Para facilitar este processo de partilha, o vídeo sofre algumas etapas de processamento, de forma a ser obtido um vídeo reduzido e as respetivas características do conteúdo. Deste modo, a solução proposta contempla um sistema de anotação que habilita a pesquisa por conteúdo, servindo assim como uma ferramenta versátil para a investigação nesta área. Este sistema é ainda dotado de um módulo de streaming, no qual é transmitido, em tempo real, o exame endoscópico, disponibilizando um canal de comunicação com vídeo unidirecional e áudio bidirecional, permitindo que os profissionais ausentes da sala do exame deem a sua opinião remotamente.Among the different kinds of endoscopic procedures, esophagogastroduodenoscopy plays a major role because it is the ideal method to examine the upper digestive tract, as well as to detect numerous gasteroentologic diseases. The result of such procedures is usually a written report that comprises a set of frames captured during the examination, sometimes complemented with a video. Nowadays only the images are stored along with the endoscopic report. Not storing the video may lead to discomfort concerning the patient’s well-being, as well as an increase of costs and time spent, because it is often necessary to review and validate the diagnostic hypothesis, and compare video segments in future exams. Even in the cases in which the information is stored, the lack of reutilization and share of information and videos among entities contributes, once again, for an unnecessary repetition of exams. Besides solving the problems mentioned above, the existence of an endoscopic video archive would be an asset because it would enable research and investigation activities. Furthermore it would make available exams to serve as a reference for the study of similar cases. In this work, an extended solution of acquisition, processing, archiving and diffusion of endoscopic procedures is proposed. The aim is to provide a system capable of managing all the administrative and clinical information (including audiovisual content) from its acquisition process to the searching process of previous exams, for comparison with new cases. In order to ensure compatibility of lexical information shared in the system, a standardized endoscopic vocabulary, the Minimal Standard Terminology (MST) was used. In this context, a device for the acquisition of the endoscopic video was designed (MIVbox), regardless of the endoscopic camera that is used. All the information is stored in a structured and standardized way, allowing its reuse and sharing. To facilitate this sharing process, the video undergoes some processing steps in order to obtain a summarized video and the respective content characteristics. The proposed solution provides an annotation system that enables content querying, thus becoming a versatile tool for research in this area. This system is also provided with a streaming module in which the endoscopic video is transmitted in real time. This process uses a communication channel with one-way video and two-way audio, allowing professionals absent from the exam room to give their opinion remotely