Gastroenterology

Latest AI and machine learning research in gastroenterology for healthcare professionals.

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Emerging Role of MRI-Based Artificial Intelligence in Individualized Treatment Strategies for Hepatocellular Carcinoma: A Narrative Review.

Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, with significant ...

Machine learning classification of steatogenic compounds using toxicogenomics profiles.

The transition toward new approach methodologies for toxicity testing has accelerated the developmen...

Automating liver biopsy segmentation with a robust, open-source tool for pathology research: the HOTSPoT model.

Artificial intelligence applications in liver pathology remain limited, with existing tools either n...

Integrating bulk and single cell sequencing data to identify prognostic biomarkers and drug candidates in HBV associated hepatocellular carcinoma.

Hepatitis B virus (HBV) infection is a major driver of hepatocellular carcinoma (HCC), yet the mecha...

Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.

BACKGROUND: Allogeneic hematopoietic stem cell transplantation (HSCT) is potentially curative for he...

Predicting liver metastasis in colorectal cancer patients using routine biochemical tests enhanced by machine learning.

BACKGROUND: Liver is the most common metastatic site in colorectal cancer. This study aims to evalua...

Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning.

In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep ...

Non-invasive tests of fibrosis in the management of MASLD: revolutionising diagnosis, progression and regression monitoring.

With the recent conditional approval of resmetirom by the US Food and Drug Administration, the treat...

Artificial intelligence for endoscopic grading of gastric intestinal metaplasia: advancing risk stratification for gastric cancer.

BACKGROUND AND STUDY AIMS: Endoscopic Grading of Gastric Intestinal Metaplasia (EGGIM) correlates wi...

Knowledge-augmented Patient Network Embedding-based Dynamic Model Selection for Predictive Analysis of Pediatric Drug-induced Liver Injury.

OBJECTIVE: To address the challenges of developing machine learning frameworks for Electronic Health...

Diagnostics of Autoimmune Hepatitis Enabled by Non-Invasive Clinical Proteomics.

BACKGROUND: Autoimmune hepatitis (AIH) may be difficult to diagnose and distinguish clinically and b...

FSS-ULivR: a clinically-inspired few-shot segmentation framework for liver imaging using unified representations and attention mechanisms.

Precise liver segmentation is critical for accurate diagnosis and effective treatment planning, serv...

Identification of CTSK as a TLR-related critical biomarker in liver cirrhosis via integrative bioinformatics and pathological characterization.

Liver cirrhosis (LC) is a common chronic disease worldwide with a poor prognosis, and its pathogenes...

Identification and evaluation of metabolic mRNAs and key miRNAs in colorectal cancer liver metastasis.

BACKGROUND: Colorectal cancer (CRC) represents a major global health challenge due to its high letha...

Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.

To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruc...

Deep multi-task learning framework for gastrointestinal lesion-aided diagnosis and severity estimation.

Accurate diagnosis and severity estimation of gastrointestinal tract (GT) lesions are crucial for pa...

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