Gastroenterology

Precise prevention of DEHP induced hepatic fibrosis: Early identifying high-risk populations, revealing key factors, and applying targeted intervention.

Metabolic dysfunction-associated fatty liver disease (MAFLD) poses a serious threat to human health. Hepatic fibrosis is a decisive factor in the death of MAFLD patients. Di-2-ethylhexyl phthalate (DEHP) plays an indispensable role in MAFLD. However, there are still certain shortcomings in the systematic evaluation of its dose-response relationship, key toxic mechanisms, early identification of DE...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limiting neoadjuvant chemoimmunotherapy (NACI) response prediction. We propose an interpretable multimodal framework to: (1) quantitatively map intra-/peritumoral heterogeneity via voxel-wise habitat radiomics; (2) model cross-sectional tumor biology usin...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

Concurrent AI assistance with LI-RADS classification for contrast enhanced MRI of focal hepatic nodules: a multi-reader, multi-case study.

PURPOSE: The Liver Imaging Reporting and Data System (LI-RADS) assessment is subject to inter-reader variability. The present study aimed to evaluate ...

ToBaFu: Topology-based fusion model for classification of two-dimensional cancer images.

Medical images play a pivotal role in disease diagnosis. Numerous studies on cancer image analysis focus on end-to-end deep neural networks, neglectin...

Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is the most sensitive modality for accurately establishing a tissue diagnosis in patients with solid pancreatic masse...

Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine.

Echinococcosis is a zoonotic parasitic disease characterized by its insidious nature and severe health impacts. Rapid and accurate screening is crucia...

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of ...

Integration of machine learning and SERS technology for precise classification and diagnosis of colon cancer from plasma samples.

Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning offers a transformative label-free approach for colorectal cancer detection,...

AI-Driven Fetal Liver Echotexture Analysis: A New Frontier in Predicting Neonatal Insulin Imbalance.

OBJECTIVES: To evaluate the performance of artificial intelligence (AI)-based models in predicting elevated neonatal insulin levels through fetal hepa...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh a...

Prostate MR image segmentation using a multi-stage network approach.

Prostate cancer (PCa) remains one of the most prevalent cancers among men, with over 1.4 million new cases and 375,304 deaths reported globally in 202...

Clinical implications of computer-aided real-time size estimation of colorectal polyps during colonoscopy: a prospective study.

Accurate polyp size estimation during colonoscopy is crucial for clinical decision making, follow-up, and implementation of cost-saving strategies. Ob...

Colorectal mucosal exposure area assessment using artificial intelligence: a multicenter prospective observational study.

This study proposed a new quality control indicator for colonoscopy, the cumulative colorectal mucosal exposure area (CCMEA), to assess mucosal exposu...

Bilirubin biosensors for liver disease management.

Bilirubin is a vital biomarker that plays a significant role in assessing liver function and diagnosing hemolytic disorders. While current detection m...

Identification of uridine phosphatase 1 as a potential therapeutic target in gastric cancer by integrated bioinformatics analysis and experimental validation.

Gastric cancer remains a major global health challenge, and its early diagnosis and prognosis prediction pose significant challenges to the current cl...

Screening for Barrett Esophagus and Esophageal Adenocarcinoma: Approaches and Outcomes.

Barrett esophagus (BE) is the only known histological precursor to esophageal adenocarcinoma (EAC). The incidence of EAC has risen significantly over ...

Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis.

INTRODUCTION: Metabolic dysfunction-associated steatotic liver disease (MASLD) can progress to metabolic dysfunction-associated steatohepatitis (MASH)...

Advanced liver fibrosis detection using a two-stage deep learning approach on standard T2-weighted MRI.

OBJECTIVES: To develop and validate a deep learning model for automated detection of advanced liver fibrosis using standard T2-weighted MRI. METHODS: ...

Artificial Intelligence for Quantifying Endoscopic Mucosal Ulceration in Crohn's Disease.

BACKGROUND & AIMS: Endoscopic scoring of Crohn's disease (CD) is challenging, as mucosal disease is patchy with highly variable morphology, size, and ...