Gastroenterology

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in u...

T2-weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study.

PURPOSE: To compare image quality and clinical utility of a T2-weighted (T2W) 3-dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).

TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspiciou...

Predicting hepatocellular carcinoma survival with artificial intelligence.

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfa...

AI-Cirrhosis-ECG (ACE) score for predicting decompensation and liver outcomes.

BACKGROUND & AIMS: Accurate prediction of disease severity and prognosis are challenging in patients with cirrhosis. We evaluated whether the deep lea...

Protocol for AI-based segmentation and quantification of interstitial cells of Cajal in murine gastric muscle.

Interstitial cells of Cajal (ICCs), pacemaker and neuromodulator cells in the gastrointestinal (GI) tract, play an important role in GI motility. Howe...

Automated identification of incidental hepatic steatosis on Emergency Department imaging using large language models.

BACKGROUND: Hepatic steatosis is a precursor to more severe liver disease, increasing morbidity and mortality risks. In the Emergency Department, rout...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have examined how sepsis and its onset timing affect mort...

A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture.

OBJECTIVE: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Ma...

Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.

Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective st...

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Colorectal cancer (CRC) is the most common malignancy in the digestive system, with a lower 5-year overall survival rate. There is increasing evidence...

Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.

Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap betw...

G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning.

BACKGROUND: Hepatocellular carcinoma (HCC) treatment remains challenging, particularly for immune checkpoint inhibitors (ICIs) non-response patients. ...

Accelerated Multi-b-Value DWI Using Deep Learning Reconstruction: Image Quality Improvement and Microvascular Invasion Prediction in BCLC Stage A Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: To investigate the effect of accelerated deep-learning (DL) multi-b-value DWI (Mb-DWI) on acquisition time, image quality, a...

Quality review and content analysis of liver complications mobile apps in Iran: A statistical and machine learning approach.

BACKGROUND: Liver disease accounts for 4 % of global mortality. The advent of mobile technology has introduced a novel domain in liver disease managem...

Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the managem...

Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.

BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep lear...

CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy.

RATIONALE AND OBJECTIVES: Immunotherapy combined with chemotherapy has improved outcomes for some esophageal squamous cell carcinoma (ESCC) patients, ...

FibrAIm - The machine learning approach to identify the early stage of liver fibrosis and steatosis.

BACKGROUND: Early recognition of steatosis (fatty liver) and fibrosis in liver health is crucial for effectively managing and preventing the possibili...