Gastroenterology

The CT colonography radiology and data system: history, updates, and future directions.

Computed tomography colonography, also known as virtual colonoscopy, is a minimally invasive imaging technique developed in the early 1990s to evaluate the colon for polyps, cancer, and other abnormalities. Advances in multidetector computed tomography, bowel preparation protocols, and three-dimensional reconstruction rapidly improved diagnostic performance. Landmark trials demonstrating sensitivi...

Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During Mechanical Ventilation.

OBJECTIVE: Current methods for estimating inspiratory muscle pressure (Pmus) during mechanical ventilation are either invasive or dependent on occlusion maneuvers. A noninvasive artificial intelligence (AI) algorithm estimating in real-time the amplitude and timing of Pmus, enabling continuous monitoring of patient effort, driving pressure, and synchrony with the ventilator was designed, and its p...

Blocking MOXD1-derived ACOX1 peroxisome trafficking suppresses metabolic dysfunction-associated steatohepatitis.

BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) has become a major global health burden yet effective pharmacological treatments r...

Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.

Hepatitis C virus (HCV) infection remains a leading cause of liver cirrhosis and hepatocellular carcinoma globally, affecting approximately 50 million...

Maximizing pancreatic carcinoma classification performance using parrot optimized vision transformer.

Pancreatic cancer is a rare kind of cancer that is detected during the final stages. This is because the symptoms are very common and also do not show...

Machine learning and artificial intelligence in liquid biopsy-based early detection of pancreatic cancer: a scoping review.

Pancreatic ductal adenocarcinoma (PDAC) presents as a cancer with an especially poor prognosis, largely due to the challenges surrounding its early di...

Development and validation of a novel YOLOv5-based artificial intelligence model for gastric mucosal lesion detection.

BACKGROUND AND AIMS: Artificial intelligence (AI) has been widely used in endoscopic diagnosis; however, an AI model capable of comprehensively diagno...

Half-dose contrast media protocol using 70 kVp abdominal dynamic CT with super-resolution deep learning reconstruction: Evaluation of image quality and contrast performance.

OBJECTIVE: To evaluate contrast enhancement and image quality in 70 kVp abdominal dynamic CT using super-resolution deep learning reconstruction (SR-D...

LRF-CNN: An explainable lightweight receptive field-based CNN for colorectal cancer histopathological image classification.

Colorectal cancer (CRC) screening and diagnosis rely on histopathological assessment, but many high-performing deep learning (DL) models remain comput...

Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.

OBJECTIVE: To develop and validate a transformer-based deep learning-radiomics model for the non-invasive preoperative discrimination of tumor deposit...

Comparing the performance of statistical and machine learning survival models in predicting timing and determinants of postnatal care in Rwanda.

BACKGROUND: Postnatal care (PNC) remains the least utilized component of the maternal care continuum despite its critical role in preventing maternal ...

Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.

BACKGROUND: T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic ...

Artificial intelligence-based vascular pattern profiling predicts prognosis and therapeutic response in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by active angiogenesis and heterogeneous vascular patterns. However, vascular pattern prof...

Development and Validation of a Deep Learning-enabled Single Breath-hold Abbreviated MRI Protocol for Hepatocellular Carcinoma Diagnosis.

Purpose To develop a deep learning-enabled single breath-hold abbreviated MRI (DL-SBH-aMRI) protocol for hepatocellular carcinoma (HCC) diagnosis. Mat...

Antibiotic therapy in febrile neutropenia in haematological patients: current considerations and future challenges.

The management of febrile neutropenia (FN) in oncohematological patients is undergoing a paradigm shift driven by a deeper understanding of patients' ...

ToxiGuard: an AOP-guided mechanistically interpretable framework for multi-organ toxicity prediction.

Accurate prediction of organ-specific toxicity with mechanistic interpretability remains a central challenge in chemical safety assessment and transla...

Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI.

OBJECTIVE: Tumor budding (TB) is a histopathological marker of aggressive behavior and poor prognosis in rectal cancer (RC), yet not reliably evaluate...

Deep Learning Algorithm Based on Contrast-Enhanced Ultrasound Potentially Optimizes Treatment Strategies for Solitary Primary Hepatocellular Carcinoma.

OBJECTIVE: We aimed to propose a prognostic framework using a dual-branch Vision Transformer (ViT) deep learning (DL) architecture for stratifying rec...

Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.

Esophageal cancer is a highly aggressive malignancy where early detection is critical for survival. However, early-stage lesions typically present sub...

AI-driven volumetric approach for automatic chemotherapy response assessment in colorectal liver metastases.

OBJECTIVES: This study evaluates the clinical utility of an artificial intelligence (AI)-driven volumetric approach for assessing treatment response i...