Transplantation

Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool.

Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK's transplant selectio...

Endovascular robotics: technical advances and future directions.

Endovascular interventions excel in treating cardiovascular diseases in a minimally invasive manner, showing improved outcomes over open techniques. However, challenges related to precise navigation - still relying on 2D fluoroscopy - persist. This review examines the role of robotics, highlighting commercial and research platforms, while exploring emerging trends like MRI compatibility, enhanced ...

Association of and gene polymorphisms and ERAP2 protein with the susceptibility and severity of rheumatoid arthritis in the Ukrainian population.

INTRODUCTION: Rheumatoid arthritis (RA) is a long-term autoimmune disorder that primarily affects joints. Although RA is chiefly associated with HLA c...

Challenges in standardizing preimplantation kidney biopsy assessments and the potential of AI-Driven solutions.

PURPOSE OF REVIEW: This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological ...

The promise of AI in healthcare: transforming communication and decision-making for patients.

By addressing communication gaps, the integration of AI tools in healthcare has a greater ability to improve decision-making and to empower patients w...

Assessing donor kidney function: the role of CIRBP in predicting delayed graft function post-transplant.

INTRODUCTION: Delayed graft function (DGF) shortens the survival time of transplanted kidneys and increases the risk of rejection. Current methods are...

A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.

BACKGROUND: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects ...

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.

Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher...

Active Disturbance Rejection Control for an automotive suspension system based on parameter tuning using a fuzzy technique.

Road surface roughness is the cause of vehicle vibration, which is considered a system disturbance. Previous studies on suspension system control ofte...

Integrated of Hyperspectral Imaging and Machine Learning Algorithms for Nondestructive Detection of Therapeutic Properties of Plants.

The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making ...

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further res...

Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.

This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patien...

Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

BACKGROUND: Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, thei...

Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.

Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and ac...

Diagnostic and prognostic perspectives of Fabry disease via fiber evanescent wave spectroscopy advanced by machine learning.

Fabry disease (FD) is a rare disorder resulting from a genetic mutation characterized by the accumulation of sphingolipids in various cells throughout...

Segmentation of the iliac crest from CT-data for virtual surgical planning of facial reconstruction surgery using deep learning.

BACKGROUND AND OBJECTIVES: For the planning of surgical procedures involving the bony reconstruction of the mandible, the autologous iliac crest graft...

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learnin...

Retrospective cohort study based on the MIMIC-IV database: analysis of factors influencing all-cause mortality at 30 days, 90 days, 1 year, and 3 years in patients with different types of stroke.

OBJECTIVE: This study aims to evaluate key factors influencing the short-term and long-term prognosis of stroke patients, with a particular focus on v...

Effects of feeding of vitamin C on embryonic development, hatching process, and chick rectal temperature of broiler embryos.

Maternal nutritional status plays a crucial role in embryonic development and has persistent effects on postnatal chicks. Vitamin C (VC) plays an impo...

Visceral condition assessment through digital tongue image analysis.

Traditional Chinese medicine (TCM) has long utilized tongue diagnosis as a crucial method for assessing internal visceral condition. This study aims t...