Transplantation

Enhancing DILI toxicity prediction through integrated graph attention (GATNN) and dense neural networks (DNN).

Drug-induced liver injury (DILI) toxicity is a condition when drugs have a destructive effect on the liver organ. The prediction of this toxicity becomes crucial in the drug development process to guarantee that drugs are safe from toxicity. Assessment is usually carried out in the conventional laboratory, which causes a high cost in materials and time. To help solve the problem, computational tec...

Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.

Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. Artificial intelligence quantitative computed tomographic (AI-QCT) can potentially identify subclinical atherosclerosis in cases deemed normal by read...

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of ...

Estimation of Ganciclovir Exposure in Adults Transplant Patients by Machine Learning.

INTRODUCTION: Valganciclovir, a prodrug of ganciclovir (GCV), is used to prevent cytomegalovirus infection after transplantation, with doses adjusted ...

Nonlocal models in biology and life sciences: Sources, developments, and applications.

Mathematical modeling is one of the fundamental techniques for understanding biophysical mechanisms in developmental biology. It helps researchers to ...

Design and implementation of an independent-drive bionic dragonfly robot.

Bionic flapping wing robots achieve flight by imitating animal flapping wings, which are safe, flexible, and efficient. Their practicality and human-m...

Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.

Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze ...

Pre-transplant and transplant parameters predict long-term survival after hematopoietic cell transplantation using machine learning.

BACKGROUND: Allogeneic hematopoietic stem transplantation (allo-HSCT) constitutes a curative treatment for various hematological malignancies. However...

Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.

BACKGROUND: The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for org...

Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. Thi...

Plant-inspired decentralized controller for robust orientation control of soft robotic manipulators.

Due to the complexity of deformations in soft manipulators, achieving precise control of their orientation is particularly challenging, especially in ...

An extensive experimental analysis for heart disease prediction using artificial intelligence techniques.

The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality gl...

A 3D medical image segmentation network based on gated attention blocks and dual-scale cross-attention mechanism.

In the field of multi-organ 3D medical image segmentation, Convolutional Neural Networks (CNNs) are limited to extracting local feature information, w...

Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study.

PURPOSE: Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological proc...

Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture-Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification.

Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and impro...

Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the managem...

Advanced prognostic modeling with deep learning: assessing long-term outcomes in liver transplant recipients from deceased and living donors.

BACKGROUND: Predicting long-term outcomes in liver transplantation remain a challenging endeavor. This research aims to harness the power of deep lear...

Advanced image preprocessing and context-aware spatial decomposition for enhanced breast cancer segmentation.

The segmentation of breast cancer diagnosis and medical imaging contains issues such as noise, variation in contrast, and low resolutions which make i...

Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.

This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep lear...

A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation.

Although deep learning has revolutionized abdominal multi-organ segmentation, its models often struggle with generalization due to training on small-s...