Latest AI and machine learning research in gastroenterology for healthcare professionals.
This study addresses multiple challenges in applying forensic genealogy to Chinese populations by exploring novel kinship classification strategies based on machine learning (ML) and deep learning (DL). Utilizing Infinium Asian Screening Array (ASA) microarray integrated with Han Chinese reference data from the 1000 Genomes Project, we simulated 70,000 pairs of first- to fifth-degree relatives and...
Robotic-assisted surgery (RAS) has evolved from a procedural innovation into an increasingly integrated component of contemporary digital surgical ecosystems. Nevertheless, most current Health Technology Assessment (HTA) frameworks continue to evaluate robotic systems primarily through comparator-based models focused on isolated perioperative and oncological outcomes. In this EFISDS-TROGSS positio...
BACKGROUND: Steatotic liver disease affects 40% of nonobese individuals, but existing screening tools inadequately detect and stage disease severity i...
Gastrointestinal (GI) diseases such as polyps, esophagitis, and ulcerative colitis pose significant diagnostic challenges due to subtle visual pattern...
Gastrointestinal (GI) diseases represent a major global health burden, making accurate and early diagnosis critical for improved clinical outcomes. We...
Rebleeding is a severe complication following recovery from esophageal variceal bleeding (EVB), yet robust predictive tools for assessing post-treatme...
Polyp segmentation in colonoscopy images plays a critical role in the early detection and treatment of colorectal cancer. Although deep learning-based...
BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinica...
BACKGROUND: Graft-versus-host disease (GVHD) remains a critical complication affecting patients undergoing hematopoietic stem cell transplantation (HS...
Acute respiratory failure (ARF) is a common organ failure in acute pancreatitis (AP) with high mortality. This study used machine learning to predict ...
BACKGROUND: Prognostic models for hepatocellular carcinoma (HCC) may have limited accuracy. We aimed to construct and validate a novel prognostic mode...
BACKGROUND AND AIMS: Artificial intelligence has increasingly enabled large-scale analysis of clinical documentation, offering new opportunities to im...
In the field of oncology research, patient data typically encompasses diverse multimodal characteristics, including age, survival status, radiological...
While the biochemical impacts of heavy metals on aquatic organisms are well-documented, quantitative behavioral analyses remain limited. This study in...
Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...
Hepatocellular carcinoma is a leading cause of cancer mortality globally. Liver transplantation is considered the best curative treatment for selected...
Data scarcity, inter-institutional stain variability, and privacy constraints are major challenges impeding the development of generalizable artificia...
BACKGROUND: Elderly patients are highly susceptible to drug-drug interaction (DDI)-induced liver injury, yet comprehensive real-world evidence remains...
Deep learning models often struggle with class imbalance and low-resolution medical images, where critical spatial details and minority-class features...
BACKGROUND: In contrast to three decades ago, liver resection (LR) is now an increasingly common procedure. This study aims to evaluate changes in the...