Gastroenterology

Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification.

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum in...

Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.

PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This study aimed to evaluate and compare the effectiveness of using three-dimensional (3D) CT body composition measures from the entire lumbar spine levels...

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.

BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clini...

Machine learning based prediction model for bile leak following hepatectomy for liver cancer.

OBJECTIVE: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver ...

Diagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.

OBJECTIVES: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus sev...

Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.

BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concer...

Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis.

BACKGROUND: Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic dis...

Impact of Sleep Fragmentation and Arousal on Nonalcoholic Fatty Liver Disease in Patients with Obstructive Sleep Apnea: A Cross-Sectional Study.

PURPOSE: Obstructive sleep apnea (OSA) is a contributing factor to nonalcoholic fatty liver disease (NAFLD). This study aimed to investigate the clini...

Combining solubilization and controlled release strategies to prepare pH-sensitive solid dispersion loaded with albendazole: and studies.

Albendazole (ABZ), classified as a class II basic drug under the Biopharmaceutics Classification System (BCS), is widely recognized for its therapeuti...

Indole 3-acetate and response to therapy in borderline resectable or locally advanced pancreatic cancer.

BACKGROUND/AIMS: It was recently reported that a higher concentration of the bacterially produced metabolite indole 3-acetate (3-IAA) in blood was lin...

Pinpointing the integration of artificial intelligence in liver cancer immune microenvironment.

Liver cancer remains one of the most formidable challenges in modern medicine, characterized by its high incidence and mortality rate. Emerging eviden...

Comparative transcriptomic and molecular biology analyses to explore potential immune responses to challenge in .

is a significant pathogen affecting shrimp and crab farming, particularly strains carrying genes associated with acute hepatopancreatic necrosis synd...

Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.

One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endosco...

Machine learning-based integration develops a disulfidptosis-related lncRNA signature for improving outcomes in gastric cancer.

Gastric cancer remains one of the deadliest cancers globally due to delayed detection and limited treatment options, underscoring the critical need fo...

Machine learning to predict the decision to perform surgery in hepatic echinococcosis.

BACKGROUND: Cystic echinococcosis (CE) is a significant public health issue, primarily affecting the liver. While several management strategies exist,...

A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health.

BACKGROUND: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily...

A two-phase transfer learning framework for gastrointestinal diseases classification.

Gastrointestinal (GI) disorders are common and often debilitating health issues that affect a significant portion of the population. Recent advancemen...

A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B.

BACKGROUND AND AIMS: This study aims to develop and validate a machine learning (ML) model predicting hepatocellular carcinoma (HCC) in chronic hepati...

Development and Validation of a Multimodal Machine Learning Model for Diagnosing and Assessing Risk of Crohn's Disease in Patients With Perianal Fistula.

BACKGROUND: Diagnosis of Crohn's disease (CD) can pose challenges, particularly when perianal fistula is the initial presentation.

Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes oc...