Gastroenterology

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

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Review of Deep Learning Performance in Wireless Capsule Endoscopy Images for GI Disease Classification.

Wireless capsule endoscopy is a non-invasive medical imaging modality used for diagnosing and monito...

Optimized machine learning model for predicting unplanned reoperation after rectal cancer anterior resection.

BACKGROUND: Unplanned reoperation (URO) after surgery adversely affects the quality of life and prog...

AI-Safe-C score: Assessing liver-related event risks in patients without cirrhosis after successful direct-acting antiviral treatment.

BACKGROUND & AIMS: Direct-acting antivirals (DAAs) have considerably improved chronic hepatitis C (H...

Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients,...

Characterizing the Coefficient of Friction Between a Capsule Robot and the Colon.

OBJECTIVE: A capsule robot (CR) with an onboard active locomotion mechanism, has been developed as a...

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images.

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a...

Predictive modeling of hepatitis B viral dynamics: a caputo derivative-based approach using artificial neural networks.

A fractional model for the kinetics of hepatitis B transmission was developed. The hepatitis B virus...

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mo...

Effect of a novel artificial intelligence-based cecum recognition system on adenoma detection metrics in a screening colonoscopy setting.

BACKGROUND AND AIMS: Cecal intubation in colonoscopy relies on self-reporting. We developed an artif...

Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk.

BACKGROUND: The existing predictive models for metabolic-associated fatty liver disease (MAFLD) poss...

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning.

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD...

The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.

Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management o...

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes.

BACKGROUND: Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each...

Deciphering the cytotoxicity of micro- and nanoplastics in Caco-2 cells through meta-analysis and machine learning.

Plastic pollution, driven by micro- and nanoplastics (MNPs), poses a major environmental threat, exp...

Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.

BACKGROUND: This study aims to establish a predictive model for assessing the risk of esophageal can...

Clinical feasibility of deep learning-accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla.

PURPOSE: To assess the feasibility of the single-shot turbo spin echo sequence using deep learning-b...

Boundary-aware convolutional attention network for liver segmentation in ultrasound images.

Liver ultrasound is widely used in clinical practice due to its advantages of non-invasiveness, non-...

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