Pancreatic Diseases

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

BACKGROUND: The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma and defensive attitudes. We aimed to develop a machine learning-based classification model, named Firefighter PTSD Latent Risk Evaluation (FLARE), to identify firefighters at risk of probable PTSD based on demographic and clinically relevant psychosoc...

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature of healthcare data and the need for privacy-preserving solutions. This paper proposes FEDI-CODE, a Federated and Causally Informed Dementia Estimation framework that integrates deep learning, federated learning, and counterfactual inference to predi...

PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.

Accurate delineation of pancreatic tumors on Magnetic Resonance Imaging (MRI) is important for diagnosis, radiotherapy treatment planning, and outcome...

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cell-in-cell (CIC) structures are among the most intriguing cellular phenomena occasionally observed in human cancer specimens. Once regarded as incid...

Semi-automatic mask guidance enhances 3D tumor segmentation in medical imaging.

BACKGROUND: Accurate tumor segmentation is essential for early diagnosis, treatment planning, and prognostic evaluation. Although manual annotation ca...

Development of Data-Driven Models for Just-in-Time Digital Self-Management Advice to Improve Physical Functioning in Hip and Knee Osteoarthritis: Protocol for the e-cOAch Cross-Over Study.

BACKGROUND: Clinical guidelines recommend a stepped-care strategy for patients with hip and knee osteoarthritis that begins with nonoperative approach...

Evaluating machine learning algorithms at predicting developmental trajectories using sequential dataset truncation of voluntary alcohol consumption in adolescent mice.

BACKGROUND: Adolescent alcohol consumption is a known risk factor for developing alcohol use disorder (AUD) in adulthood, but individual susceptibilit...

A HRH1-YAP1 feedback loop drives pancreatic cancer progression and predicts therapeutic response.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal prognosis, characterized by an immunosuppressive tumor microenviro...

Integrating 2.5D multi-modal CT imaging with serum triglycerides for early risk stratification in hypertriglyceridemia-induced acute pancreatitis: A multicenter study.

OBJECTIVES: To develop and externally validate a clinical-radiological framework that fuses 2.5D deep learning features from dual-phase computed tomog...

Establishing discard criteria for lead aprons using deep learning-based quantification of defect area on X-ray fluoroscopic video.

To establish an objective discard framework for lead aprons, defect areas quantified from X-ray fluoroscopy videos were linked to their corresponding ...

Pancreatic organoids and organoid-on-a-chip platforms: from disease modeling to precision therapy.

BACKGROUND: Pancreatic diseases, including diabetes, pancreatic ductal adenocarcinoma, pancreatitis, and cystic fibrosis, impose a substantial clinica...

Machine learning model for early prediction of acute respiratory failure in acute pancreatitis: Multicenter validation.

Acute respiratory failure (ARF) is a common organ failure in acute pancreatitis (AP) with high mortality. This study used machine learning to predict ...

A recyclable, mechanically robust, and anti-freezing chitosan/polyvinyl alcohol/polyaniline conductive hydrogel for multifunctional sensing application.

Conductive hydrogels have demonstrated fascinating application potential in flexible electronics. However, conductive hydrogels often exhibit poor mec...

Multi-label dynamic diagnosis of pancreatic diseases using AI-enhanced endoscopic ultrasound: a multi-cohort real-world study.

BACKGROUND: Artificial intelligence (AI) is a promising tool for pancreatic disease diagnosis using Endoscopic Ultrasound (EUS) images. But, current m...

Integrative spatiotemporal transcriptomics identifies a liver metastasis-related initial cell population associated with the SEMA3A-NRP1 axis in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy in which liver metastasis represents the principal determinant of po...

Prediction of Alzheimer's disease risk factors from retinal images via deep learning: Development and validation of biologically relevant morphological associations in the UK Biobank.

BackgroundThe systemic, metabolic, lifestyle factors have established associations with Alzheimer's disease (AD) through epidemiologic and AD-specific...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigastric veins on contrast-enhanced CT images in patie...

Machine learning-based prediction of polyvinyl alcohol product viscosity and design of optimal process conditions.

We developed a soft sensor to predict polyvinyl alcohol viscosity and designed process conditions to reach the target range. Using squared and cross t...

An Aptamer-Engineered Phenotyping System for Extracellular Vesicles Based on Honeycomb-like Melamine-Formaldehyde Microspheres and Carbon Dot Nanozymes.

Molecular phenotyping of extracellular vesicles (EVs) holds promise for noninvasive cancer diagnosis; however, current methodologies encounter limitat...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MRI scanning. We aim to develop classifier models to...