Latest AI and machine learning research in gastroenterology for healthcare professionals.
Artificial intelligence using deep learning has been applied to gastrointestinal disorders for the detection, classification, and delineation of various lesion images. With the accumulation of enormous medical records, the evolution of computation power with graphic processing units, and the widespread use of open-source libraries in large-scale machine learning processes, medical artificial intel...
The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (...
OBJECTIVES: A superficial nonampullary duodenal epithelial tumor (SNADET) is defined as a mucosal or submucosal sporadic tumor of the duodenum that do...
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to i...
BACKGROUND: High-resolution MRI is regarded as the best method to evaluate whether there is an involved circumferential resection margin in rectal can...
INTRODUCTION: Thoracic diseases include a variety of common human primary malignant tumors, among which lung cancer and esophageal cancer are among th...
INTRODUCTION: Peritoneal metastasis (PM) is a frequent condition in patients presenting with gastric cancer, especially in younger patients with advan...
The rapid development of computer technologies brings us great changes in daily life and work. Artificial intelligence is a branch of computer science...
PURPOSE: The current systematic review and meta-analysis aimed to compare Laparoscopic Distal Pancreatectomy (LPD) with Robotic Distal Pancreatectomy ...
Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and ...
PURPOSE: To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and MRI liver observations using the liver imaging rep...
Most colorectal polyps are diminutive, and malignant potential for these polyps is uncommon, especially for those in the rectosigmoid. However, many d...
The diagnosis of disease often requires analysis of a biopsy. Many diagnoses depend not only on the presence of certain features but on their location...
BACKGROUND/AIM: To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy.
PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well...
This chapter proposes a method to detect metastatic liver cancer from X-ray CT images using a convolutional neural network (CNN). The proposed method ...
With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning c...
OBJECTIVE: The aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer.
PURPOSE: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant r...