Artificial Intelligence in Gastrointestinal Endoscopy.

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully

Machine learning methods for histopathological image analysis

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.Comment: 23 pages, 4 figure

Automated extraction of genes associated with antibiotic resistance from the biomedical literature

The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Whole-genome sequencing is emerging as a fast alternative to resistance prediction, by considering the presence/absence of certain genes. A lot of research has focused on determining which bacterial genes cause antibiotic resistance and efforts are being made to consolidate these facts in knowledge bases (KBs). KBs are usually manually curated by domain experts to be of the highest quality. However, this limits the pace at which new facts are added. Automated relation extraction of gene-antibiotic resistance relations from the biomedical literature is one solution that can simplify the curation process. This paper reports on the development of a text mining pipeline that takes in English biomedical abstracts and outputs genes that are predicted to cause resistance to antibiotics. To test the generalisability of this pipeline it was then applied to predict genes associated with Helicobacter pylori antibiotic resistance, that are not present in common antibiotic resistance KBs or publications studying H. pylori. These genes would be candidates for further lab-based antibiotic research and inclusion in these KBs. For relation extraction, state-of-the-art deep learning models were used. These models were trained on a newly developed silver corpus which was generated by distant supervision of abstracts using the facts obtained from KBs. The top performing model was superior to a co-occurrence model, achieving a recall of 95%, a precision of 60% and F1-score of 74% on a manually annotated holdout dataset. To our knowledge, this project was the first attempt at developing a complete text mining pipeline that incorporates deep learning models to extract gene-antibiotic resistance relations from the literature. Additional related data can be found at https://github.com/AndreBrincat/Gene-Antibiotic-Resistance-Relation-Extractio

A systematic review of the role of non-magnified endoscopy for the assessment of H. pylori infection.

Background There is growing interest in the endoscopic recognition of H. pylori infection, and application to routine practice. We present a systematic review of the current literature regarding diagnosis of H. pylori during standard (non-magnified) endoscopy, including adjuncts such as image enhancement and computer-aided diagnosis. Method The Medline and Cochrane databases were searched for studies investigating the performance of non-magnified optical diagnosis for H. pylori, or those which characterised mucosal features associated with H. pylori infection. Studies were preferred with a validated reference test as the comparator, although were included if at least one validated reference test was used. Results 20 suitable studies were identified and included for analysis. In total, 4,703 patients underwent investigation including white light endoscopy, narrow band imaging, i-scan, blue-laser imaging, and computer-aided diagnostic techniques. The endoscopic features of H. pylori infection observed using each modality are discussed and diagnostic accuracies reported. The Regular Arrangement of Collecting Venules (RAC) is an important predictor of the H. pylori naïve stomach. ‘Mosaic’ and ‘Mottled’ patterns have a positive association with H. pylori infection. The ‘Cracked’ pattern may be a predictor of an H. pylori negative stomach following eradication. Conclusions This review summarises the current progress made in endoscopic diagnosis of H. pylori infection. At present there is no single diagnostic approach that provides validated diagnostic accuracy. Further prospective studies are required, as is the development of a validated classification system. Early studies in Computer-Aided Diagnosis suggest potential for a high level of accuracy but real-time results are awaited

Cuantificación de glándulas en imágenes histopatológicas de cáncer gástrico

Automatic detection and quantification of glands in gastric cancer may contribute to objectively measure the lesion severity, to develop strategies for early diagnosis, and most importantly to improve the patient categorization; however, gland quantification is a highly subjective task, prone to error due to the high biopsy traffic and the experience of each expert. The present master’s dissertation is composed by three chapters that carry to an objective identification of glands. In the first chapter of this document we present a new approach for segmentation of glandular nuclei based on nuclear local and contextual (neighborhood) information “NLCI”. A Gradient-BoostedRegression-Tree classifier is trained to distinguish between glandular nuclei and non glandular nuclei. Validation was carried out using 45.702 annotated nuclei from 90 fields of view (patches) extracted from whole slide images of patients diagnosed with gastric cancer. NLCI achieved an accuracy of 0.977 and an F-measure of 0.955, while R-CNN yielded corresponding accuracy and F-measures of 0.923 and 0.719, respectively. In second chapter we presents an entire framework for automatic detection of glands in gastric cancer images. By selecting gland candidates from a binarized version of the hematoxylin channel. Next, the gland’s shape and nuclei are characterized using local features which feed a Random-Cross-validation method classifier trained previously with images manually annotated by an expert. Validation was carried out using a data-set with 1.330 from seven fields of view extracted from patients diagnosed with gastric cancer whole slide images. Results showed an accuracy of 93 % using a linear classifier. Finally, in the third chapter analyzing gland and their glandular nuclei most relevant features, since predict if a patient will survive more than a year after being diagnosed with gastric cancer. A feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy “mRMR” approach selects those features that correlated better with patient survival. A data set with 668 Fields of View (FoV), 2.076 glandular structures from 14 whole slide images were extracted from patient diagnosed with gastric cancer. Results showed an accuracy of 78.57 % using a QDA Linear & Quadratic Discriminant Analysis was training with Leave-one-out e.g training with thirteen cases and leaving a separate case to validate.La detección y cuantificación automática de las glándulas en el cáncer gástrico puede contribuir a medir objetivamente la gravedad de la lesión, desarrollar estrategias para el diagnóstico precoz y lo que es más importante, mejorar la categorización del paciente; sin embargo, su cuantificación es una tarea altamente subjetiva, propensa a errores debido al alto tráfico de biopsias y a la experiencia de cada experto. La presente disertación de maestría está compuesta por tres capítulos los cuales llevan a la cuantificación objetiva de glándulas. En el primer capítulo del documento se presenta un nuevo enfoque para la segmentación de los núcleos glandulares en base a la información nuclear local y contextual (vecindario). Se entrenó un Gradient-Boosted-Regression-Tree para distinguir entre núcleos glandulares y núcleos no glandulares. La validación se llevó con 45.702 núcleos anotados manualmente de 90 campos de visión (parches) extraídos de imágenes de biopsias completas de pacientes diagnosticados con cáncer gástrico. NLCI logró una precisión de 0.977% y un F-Score de 0.955%, mientras que fast R-CNN arrojó una precisión de 0.923% y un F-Score y 0.719%. En el segundo capítulo se presenta un marco completo para detección automática de glándulas en imágenes de cáncer gástrico. Las glándulas candidatas se seleccionan de una versión binarizada del canal de hematoxilina. A continuación, la forma y los núcleos de las glándulas se caracterizan y se alimenta un clasificador Random Cross Validation, entrenado previamente con imágenes anotadas manualmente por un experto. La validación se realizó en un conjunto de datos con 1.330 parches extraídos de siete biopsias de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 93% utilizando un clasificador lineal. Finalmente, el tercer capítulo analiza las características más relevantes de las glándulas y sus núcleos glandulares, para predecir la sobrevida a un año de un paciente diagnosticado con cáncer gástrico. Una selección de características basada en información mutua: criterios de dependencia máxima, máxima relevancia y mínima redundancia (mRMR) escogen las características correlacionadas con la supervivencia del paciente. Se extrajo un conjunto de datos con 668 campos de visión (FoV), 2.076 estructuras glandulares de 14 imágenes completas de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 76.3% usando un Análisis Discriminante Lineal y Cuadrático (QDA) y un esquema de evaluación entrenando con trece casos y dejando un caso aparte para validar.Magíster en Ingeniería Biomédica. Línea de investigación: Procesamiento de señale

Exploring ChatGPT's Potential for Consultation, Recommendations and Report Diagnosis: Gastric Cancer and Gastroscopy Reports’ Case

Artificial intelligence (AI) has shown its effectiveness in helping clinical users meet evolving challenges. Recently, ChatGPT, a newly launched AI chatbot with exceptional text comprehension capabilities, has triggered a global wave of AI popularization and application in seeking answers through human‒machine dialogues. Gastric cancer, as a globally prevalent disease, has a five-year survival rate of up to 90% when detected early and treated promptly. This research aims to explore ChatGPT's potential in disseminating gastric cancer knowledge, providing consultation recommendations, and interpreting endoscopy reports. Through experimentation, the GPT-4 model of ChatGPT achieved an appropriateness of 91.3% and a consistency of 95.7% in a gastric cancer knowledge test. Furthermore, GPT-4 has demonstrated considerable potential in consultation recommendations and endoscopy report analysis

SVSBI: Sequence-based virtual screening of biomolecular interactions

Virtual screening (VS) is an essential technique for understanding biomolecular interactions, particularly, drug design and discovery. The best-performing VS models depend vitally on three-dimensional (3D) structures, which are not available in general but can be obtained from molecular docking. However, current docking accuracy is relatively low, rendering unreliable VS models. We introduce sequence-based virtual screening (SVS) as a new generation of VS models for modeling biomolecular interactions. The SVS model utilizes advanced natural language processing (NLP) algorithms and optimizes deep $K$-embedding strategies to encode biomolecular interactions without invoking 3D structure-based docking. We demonstrate the state-of-art performance of SVS for four regression datasets involving protein-ligand binding, protein-protein, protein-nucleic acid binding, and ligand inhibition of protein-protein interactions and five classification datasets for the protein-protein interactions in five biological species. SVS has the potential to dramatically change the current practice in drug discovery and protein engineering