A novel intelligent chromo capsule endoscope for the diagnosis of neoplastic lesions in the gastrointestinal tract.

Background: Chromoendoscopy has not been fully integrated into capsule endoscopy. This study aimded to develop and validate a novel intelligent chromo capsule endoscope (ICCE).

Methods: The ICCE has two modes: a white-light imaging (WLI) mode and an intelligent chromo imaging (ICI) mode.

Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy.

BACKGROUND : Further development of deep learning-based artificial intelligence (AI) technology to automatically diagnose multiple abnormalities in small-bowel capsule endoscopy (SBCE) videos is necessary. We aimed to develop an AI model, to compare its diagnostic performance with doctors of different experience levels, and to further evaluate its auxiliary role for doctors in diagnosing multiple abnormalities in SBCE videos. METHODS : The AI model was trained using 280 426 images from 2565 patients, and the diagnostic performance was validated in 240 videos.

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

Background: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.

Methods: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set.

Novel scheme for non-invasive gut bioinformation acquisition with a magnetically controlled sampling capsule endoscope.

Objective: Intestinal flora and metabolites are associated with multiple systemic diseases. Current approaches for acquiring information regarding microbiota/metabolites have limitations. We aimed to develop a precise magnetically controlled sampling capsule endoscope (MSCE) for the convenient, non-invasive and accurate acquisition of digestive bioinformation for disease diagnosis and evaluation.

Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model.

Background & Aims: Capsule endoscopy has revolutionized investigation of the small bowel. However, this technique produces a video that is 8-10 hours long, so analysis is time consuming for gastroenterologists. Deep convolutional neural networks (CNNs) can recognize specific images among a large variety.