Gastroenterology

Automatically Measuring Kidney, Liver, and Cyst Volumes in Autosomal Dominant Polycystic Kidney Disease.

KEY POINTS: TraceOrg is a web-based tool that automatically labels kidney, liver, and cysts, reporting volumes and Mayo Imaging Classification. External validation showed high performance and good generalizability on Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease, Polycystic Kidney Disease-Research Resource Consortium, and other external datasets. Training on multiple pulse...

A multi-source similarity fusion method based on hypergraph convolutional networks and graph transformers for predicting miRNA-disease associations.

Identifying the potential miRNA-disease association (MDA) has a greater impetus to the development of drug prevention, treatment and other fields. For the traditional prediction methods, they are characterized by long time and large cost. With the continuous development of bioinformatics, the prediction of MDA using computational methods can better advance the human development in the field of MDA...

Microbiome based precision medicine through integrated multiomics and machine learning.

Gut microbiome (GME) is a dynamic ecosystem composed of diverse microorganisms with extensive functional potential that influence host physiology, end...

Few-shot learning and explainable AI for colon cancer histopathology: A prototypical network with multi-technique interpretability.

BACKGROUND: Colon cancer diagnosis from histopathology is challenging due to limited annotated data and the lack of interpretability in deep models. O...

Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI.

OBJECTIVES: To validate an artificial intelligence (AI) method for fully automated detection and alignment of focal liver lesions (FLLs) in multi-sequ...

Advanced artificial intelligence combined with SERS platforms for diagnosis and therapeutic effects of cancer in clinical applications.

Surface-Enhanced Raman Spectroscopy (SERS) has become a valuable way to detect small amounts of molecules due to its high sensitivity. Nonetheless, ap...

Predictive imaging in abdominal oncology: current trends and future directions.

While the concept of predictive imaging is not entirely new, advanced analytic tools such as radiomics and machine learning have laid the foundation f...

Construction and optimization of a LAMP-based diagnostic platform for acute hepatopancreatic necrosis disease in Penaeus vannamei.

Acute hepatopancreatic necrosis disease (AHPND) poses a major threat to global shrimp aquaculture, especially impacting Penaeus vannamei. Given the hi...

Ultrasound super-resolved hemodynamic estimation in microvessel using physics-informed neural networks and data assimilation.

BACKGROUND AND OBJECTIVE: Ultrasound super-resolution imaging (SRI) enables the visualization of microvascular structure and velocity, but enhancing t...

Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.

OBJECTIVE: This study aims to develop and validate an integrated multi-task framework for hepatocellular carcinoma analysis by combining deep learning...

Machine learning-based integration develops a lactate metabolism related gene signature for improving outcomes in pancreatic ductal adenocarcinoma.

OBJECTIVES: Given its high global mortality rate, pancreatic ductal adenocarcinoma (PDAC) remains a significant area of investigation. However, a robu...

Comprehensive multi-omics data to construct hepatocellular carcinoma pathway subtypes and classification model.

The high heterogeneity of Hepatocellular Carcinoma (HCC) severely hampers clinical outcomes. Current classifications based on gene expression profiles...

"PD-PREDICT": A Machine Learning Model for Patient Survival in Peritoneal Dialysis.

Accurate survival prediction in peritoneal dialysis (PD) patients is essential for personalized treatment planning and shared decision-making. We deve...

Long-Term Survival and Beneficiaries of Adjuvant Anti-PD-1 Therapy in Resected Hepatocellular Carcinoma.

BACKGROUND: Early and late hepatocellular carcinoma (HCC) recurrences, which are driven by residual and de novo tumors, respectively, differ in biolog...

Deep Learning-Based Motion-Compensated Reconstruction for Accelerating 4-Dimensional Magnetic Resonance Fingerprinting.

PURPOSE: To develop and validate DeepMocor, a deep learning-based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MR...

Robotic thoracic surgery: what is different in anesthesia?

PURPOSE OF REVIEW: Robotic-assisted thoracic surgery (RATS) has emerged as a transformative approach in thoracic surgery, enabling enhanced precision ...

Development of a Machine Learning-Based Predictive Model for Anastomotic Leakage Following Gastric Cancer Surgery.

BackgroundThe incidence of anastomotic leakage (AL) following radical gastrectomy for gastric cancer ranges from 2.1% to 14.6%, with mortality rates u...

Physics-informed deep learning sharpens nano diagnostics for elusive pancreatic cancer.

Pancreatic disease affects over 10% of the world population, and the most dangerous is pancreatic cancer (PC). The disease is mostly of late age of on...

Utility of multimodal deep learning model to diagnose lymph node metastasis in esophageal cancer using computed tomography and positron emission tomography images.

PURPOSE: This study aimed to assess the performance of a deep learning model using multimodal imaging for detecting lymph node metastasis in esophagea...

Blood-based Vienna 3P/5P risk models accurately predict first hepatic decompensation in compensated advanced chronic liver disease.

BACKGROUND & AIMS: Invasive measurement of hepatic venous pressure gradient (HVPG) is the gold standard for diagnosing clinically significant portal h...