Transplantation

Latest AI and machine learning research in transplantation for healthcare professionals.

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Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences.

Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions ...

Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images.

Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer ...

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 Langerha...

Light-driven bimorph soft actuators: design, fabrication, and properties.

Soft robots that can move like living organisms and adapt to their surroundings are currently in the...

Microfiltration of saline crude oil emulsions: Effects of dispersant and salinity.

Dispersants reduce oil-water interfacial tension making the separation of oil-water emulsions challe...

Machine learning predicts live-birth occurrence before in-vitro fertilization treatment.

In-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, p...

Automated measurement of hip-knee-ankle angle on the unilateral lower limb X-rays using deep learning.

Significant inherent extra-articular varus angulation is associated with abnormal postoperative hip-...

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an...

Machine learning algorithm for early detection of end-stage renal disease.

BACKGROUND: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease...

Robot-assisted Transvaginal Single-site Sacrocolpopexy for Pelvic Organ Prolapse.

STUDY OBJECTIVE: To demonstrate stepwise techniques for the successful use of the laparoscopic singl...

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools.

The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vacci...

Sodium butyrate protects against lipopolysaccharide-induced liver injury partially via the GPR43/ β-arrestin-2/NF-κB network.

BACKGROUND: Butyrate acts as a regulator in multiple inflammatory organ injuries. However, the role ...

Emerging use of machine learning and advanced technologies to assess red cell quality.

Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine res...

Risks of Muscle Atrophy in Patients with Malignant Lymphoma after Autologous Stem Cell Transplantation.

OBJECTIVE: Muscle atrophy is associated with autologous stem cell transplantation (ASCT)-related out...

Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.

BACKGROUND: Objectives were to build a machine learning algorithm to identify bloodstream infection ...

CT-ORG, a new dataset for multiple organ segmentation in computed tomography.

Despite the relative ease of locating organs in the human body, automated organ segmentation has bee...

A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with mo...

Deep learning-enabled multi-organ segmentation in whole-body mouse scans.

Whole-body imaging of mice is a key source of information for research. Organ segmentation is a prer...

A deep learning approach for sepsis monitoring via severity score estimation.

BACKGROUND AND OBJECTIVE: Sepsis occurs in response to an infection in the body and can progress to ...

Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction.

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based im...

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