Pancreatic Diseases

Artificial intelligence in biliopancreatic endoscopy: Is there any role?

Artificial intelligence (AI) research in endoscopy is being translated at rapid pace with a number of approved devices now available for use in luminal endoscopy. However, the published literature for AI in biliopancreatic endoscopy is predominantly limited to early pre-clinical studies including applications for diagnostic EUS and patient risk stratification. Potential future use cases are highli...

Machine Learning Revealed New Correlates of Chronic Pelvic Pain in Women.

Chronic pelvic pain affects one in seven women worldwide, and there is an urgent need to reduce its associated significant costs and to improve women's health. There are many correlated factors associated with chronic pelvic pain (CPP), and analyzing them simultaneously can be complex and involves many challenges. A newly developed interaction ensemble, referred to as INTENSE, was implemented to i...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

Multivariate data-based optimization of membrane adsorption process for wastewater treatment: Multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system.

Application of machine-learning methods to assess the batch adsorption of malachite green (MG) dye on chitosan/polyvinyl alcohol/zeolite imidazolate f...

Image-Based Machine Learning Algorithms for Disease Characterization in the Human Type 1 Diabetes Pancreas.

Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment....

Data-Driven Robust Control for a Closed-Loop Artificial Pancreas.

We present a fully closed-loop design for an artificial pancreas (AP) that regulates the delivery of insulin for the control of Type I diabetes. Our A...

MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.

PURPOSE: Pancreas segmentation is a difficult task because of the high intrapatient variability in the shape, size, and location of the organ, as well...

Current status of artificial intelligence analysis for endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is an essential diagnostic tool for various types of pancreatic diseases such as pancreatic tumors and chronic pancre...

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumonisins (FUM) in different points of the food chain...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). However, this operation is complex, and the peri-oper...

Training confounder-free deep learning models for medical applications.

The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging ...

Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Ou...

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study.

BACKGROUND: The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatogr...

Safety and efficacy of robot-assisted versus open pancreaticoduodenectomy: a meta-analysis of multiple worldwide centers.

The objective of the study is to compare the safety and efficacy of robot-assisted pancreaticoduodenectomy (PD) with open PD. The PubMed, EMBASE and C...

Host variables confound gut microbiota studies of human disease.

Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify ca...

Formal robotic training diminishes the learning curve for robotic pancreatoduodenectomy: Implications for new programs in complex robotic surgery.

INTRODUCTION: The learning curve associated with robotic pancreatoduodenectomy (RPD) is a hurdle for new programs to achieve optimal results. Since ea...

Use of Artificial Intelligence Deep Learning to Determine the Malignant Potential of Pancreatic Cystic Neoplasms With Preoperative Computed Tomography Imaging.

BACKGROUND: Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). However, downsides of these guid...

A deep learning framework for pancreas segmentation with multi-atlas registration and 3D level-set.

In this paper, we propose and validate a deep learning framework that incorporates both multi-atlas registration and level-set for segmenting pancreas...

The use of machine learning improves the assessment of drug-induced driving behaviour.

RATIONALE: Car-driving performance is negatively affected by the intake of alcohol, tranquillizers, sedatives and sleep deprivation. Although several ...

Discrimination of alcohol dependence based on the convolutional neural network.

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used fo...