Gastroenterology

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-based reconstruction (DLR) (HASTE) with enhanced denoising for pancreas MRI.

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-radiation, and real-time imaging. Accurate segmentation of the liver region in ultrasound images is essential for accelerating the auxiliary diagnosis of liver-related diseases. This paper proposes BACANet, a deep learning algorithm designed for real-time liver ultrasound segmentation. Our approach ...

Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT.

PURPOSE: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and e...

Machine learning predicts acute respiratory failure in pancreatitis patients: A retrospective study.

PURPOSE: The purpose of the research is to design an algorithm to predict the occurrence of acute respiratory failure (ARF) in patients with acute pan...

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironm...

AI in obstetrics: Evaluating residents' capabilities and interaction strategies with ChatGPT.

In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assess...

Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy.

BACKGROUND: Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a...

Immunohistochemistry annotations enhance AI identification of lymphocytes and neutrophils in digitized H&E slides from inflammatory bowel disease.

BACKGROUND AND OBJECTIVE: Histologic assessment of the immune infiltrate in H&E slides is vital in diagnosing and managing inflammatory bowel diseases...

Systems Biology and Machine Learning Identify Genetic Overlaps Between Lung Cancer and Gastroesophageal Reflux Disease.

One Health and planetary health place emphasis on the common molecular mechanisms that connect several complex human diseases as well as human and pla...

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality.

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Rec...

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Gemini as...

Usefulness of an Artificial Intelligence Model in Recognizing Recurrent Laryngeal Nerves During Robot-Assisted Minimally Invasive Esophagectomy.

BACKGROUND: Recurrent laryngeal nerve (RLN) palsy is a common complication in esophagectomy and its main risk factor is reportedly intraoperative proc...

Evaluation of floatability characteristics of gastroretentive tablets using VIS imaging with artificial neural networks.

Gastroretentive dosage forms are recommended for several active substances because it is often necessary for the drug to be released from the carrier ...

Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.

Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by nume...

A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.

AIMS: The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injurie...

Preoperative treatment response prediction for pancreatic cancer by multiple microRNAs in plasma exosomes: Optimization using machine learning and network analysis.

BACKGROUND/OBJECTIVES: MicroRNAs (miRNAs) are involved in chemosensitivity through their biological activities in various malignancies, including panc...

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis.

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. ...

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.

BACKGROUND: Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early a...

Precise ablation zone segmentation on CT images after liver cancer ablation using semi-automatic CNN-based segmentation.

BACKGROUND: Ablation zone segmentation in contrast-enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success...

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies.

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not prac...