Gastroenterology

Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Nelumbinis Receptaculum (NR) were hemostatic herbal medicines for treating metrorrhagia, hematuria or hemorrhoids in ethnopharmacological systems, such as Ayurveda and traditional Chinese medicines. AIM OF THE STUDY: The material basis for traditional hemostatic efficacy of NR is unclear. Thus, hemostatic component combination (HCC) of NR were screened by dual machi...

Addressing Colonoscopy Burden Due to Artificial Intelligence Devices for Polyp Detection.

Skill degradation, false-positive burden, and translational gap of artificial intelligence-assisted colonoscopy.

Automated Segmentation of Liver and Liver Tumors with SwinUNETR and UNET Neural Networks on 18F-FDG PET/CT.

OBJECTIVES: To develop and evaluate automated segmentation models for the liver and hepatic tumors on 18F-fluorodeoxyglucose positron emission tomogra...

Integrating lesser omentum adipose CT in dual-phase tumor imaging: A multi-label deep learning framework for preoperative microvascular invasion prediction and survival analysis in hepatocellular carcinoma.

BACKGROUND AND OBJECTIVE: Accurate preoperative prediction of microvascular invasion (MVI) and survival risk is essential for personalized treatment i...

Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease.

OBJECTIVES: Exclusive enteral nutrition (EEN) is a first-line treatment for induction of remission in luminal pediatric Crohn disease (pCD). However, ...

MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study.

PURPOSE: Accurate interpretation of CT scans after pancreatic resection is crucial for detecting abnormalities, including postoperative complications ...

Acetyl tributyl citrate induces intestinal toxicity by regulating the IDH2/NF-κB pathway and lipid peroxidation.

In recent years, the increasing prevalence of environmental pollutants has raised concerns about their potential role in intestinal-related diseases. ...

Clinical Efficacy of Real-Time Artificial Intelligence-Assisted Colonoscopy in Colorectal Polyp Detection: A Prospective Multicenter Randomized Controlled Trial.

BACKGROUND/AIMS: Early detection and removal of colon polyps are critical for preventing colorectal cancer. Computer-aided detection (CADe) systems ha...

Morphological characterization and machine learning-based hyperspectral identification of naturally pigmented traditional Chinese starches.

As an intangible cultural heritage, food products derived from naturally pigmented traditional starches are facing a market trust crisis due to the ad...

Colorectal Surgery Education in 2040.

The Accreditation Council for Graduate Medical Education (ACGME) recently conducted their 10-year specialty-specific revision of the colon and rectal ...

Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.

OBJECTIVE: This systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quan...

Adapt or specialize? A comprehensive evaluation of adapted SAM versus task-specific CNNs for fetal abdominal segmentation.

BACKGROUND: The fetal abdomen is crucial in prenatal screening, offering key insights into fetal growth and congenital anomalies. However, segmenting ...

Advances in antituberculosis drug-induced liver injury: Molecular mechanisms, genetic insights, and emerging animal-free prediction models.

Approximately 10 million tuberculosis patients worldwide require drug therapies annually. However, prolonged use of tuberculocidal drugs in combinatio...

An accurate, straightforward computer vision algorithm for optimal tumor-feeding visualization in cone-beam computed tomography hepatic arteriography: A preliminary study.

AIM: Although standardized 3D volume rendering techniques (VRT) and embolization guidance visualize and identify tumor-feeding arteries, current vesse...

An Algorithm to Avoid Missed Bowel Injuries in Blunt Abdominal Trauma Patients.

UNLABELLED: Bowel injuries following blunt abdominal trauma (BAT) are evasive to detect clinically and with current imaging modalities. These injuries...

Development and validation of a personalized web-based calculator of aggressive recurrence after surgery for early-stage hepatocellular carcinoma by machine learning.

BACKGROUND: Aggressive recurrence (AR) is an important factor affecting prognosis after surgery for hepatocellular carcinoma (HCC). This study aimed t...

Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.

UNLABELLED: Liver fibrosis, a major histopathological indicator of chronic liver injury, is also a key feature of metabolic dysfunction-associated ste...

Detection of peritoneal, ovarian, and bowel endometriosis using FTIR spectroscopy and machine learning.

This study evaluated the diagnostic potential of Fourier-transform infrared (FTIR) spectroscopy combined with machine learning for the detection of ov...

Bridging the variant-to-function gap in type 2 diabetes: advances and challenges.

Type 2 diabetes is a polygenic, heterogeneous disease affecting over 530 million individuals worldwide, a number projected to rise to 1.3 billion by 2...