Gastroenterology

Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.

GI diseases are one of the leading causes of morbidity and mortality worldwide, and early and accura...

A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study.

This study aims to identify depressive risks in elderly individuals with subjective cognitive declin...

Predicting mortality risk in Alzheimer's disease using machine learning based on lifestyle and physical activity.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, significantly impacts patient su...

Intrahepatic cholestasis of pregnancy.

Intrahepatic cholestasis of pregnancy is the most common pregnancy-related liver disease, manifestin...

Preparing clinical research data for artificial intelligence readiness: insights from the National Institute of Diabetes and Digestive and Kidney Diseases data centric challenge.

OBJECTIVES: The success of artificial intelligence (AI) and machine learning (ML) approaches in biom...

Minimal Ablative Margin Quantification Using Hepatic Arterial Versus Portal Venous Phase CT for Colorectal Metastases Segmentation: A Dual-center, Retrospective Analysis.

OBJECTIVE: To compare the predictive value of minimal ablative margin (MAM) quantification using tum...

Dynamic Early Survival Prediction Model for Hepatocellular Carcinoma Patients Treated With Atezolizumab and Bevacizumab: A Longitudinal Deep Learning Analysis.

BACKGROUND: Atezolizumab combined with bevacizumab has become the standard first-line systemic thera...

CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study.

BACKGROUND: Noninvasive and precise tools for treatment response estimation in patients with high-ri...

A commentary on "application of Artificial Intelligence lesion labeling system-assisted endoscopic submucosal dissection for the treatment of esophageal lesions in a low-volume center: a prospective cohort study".

Letter to the editor: commentary on a multimodal artificial intelligence model for early detection of hepatocellular carcinoma.

Prospective multicenter validation of a machine learning model for predicting anastomotic leakage in patients with gastric adenocarcinoma undergoing total or proximal gastrectomy.

OBJECTIVE: Predicting esophago-gastric and esophagojejunal anastomotic leakage (AL) is inherently ch...

Development of a deep learning model for T1N0 gastric cancer diagnosis using 2.5D radiomic data in preoperative CT images.

Early detection and precise preoperative staging of early gastric cancer (EGC) are critical. Therefo...

Artificial intelligence in pancreatic intraductal papillary mucinous neoplasm imaging: A systematic review.

Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management o...

Enhanced gastrointestinal disease classification using a convvit hybrid model on endoscopic images.

Endoscopy is a procedure that allows examination of the gastrointestinal system, including the stoma...

Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.

BACKGROUND: Detecting malignant transformation of sinonasal inverted papilloma (SIP) into squamous c...

Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study.

This study aimed to develop and compare fusion models combining sub-regional radiomics with multicha...

Development and validation of a nomogram model to predict postoperative delirium after resection of esophageal cancer.

The study aimed to establish and validate a nomogram model to predict postoperative delirium (POD) a...

Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma.

Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenoc...

Comparison Between Cox Proportional Hazards and Machine Learning Models for the Prognostication of Recurrence and Survival Following Liver Resection for Hepatocellular Carcinoma.

BACKGROUND: A robust prognostication model after liver resection for hepatocellular carcinoma (HCC) ...

Addressing bias in biomarker discovery for inflammatory bowel diseases: A multi-faceted analytical approach.

Xiang-Guang et al. investigate the identification of novel biomarkers linked to M1 macrophage infilt...

Application of image guided analyses to monitor fecal microbial composition and diversity in a human cohort.

The critical role of gut microbiota in human health and disease has been increasingly illustrated ov...