Transplantation

A comprehensive analysis of m6A/m7G/m5C/m1A-related gene expression and immune infiltration in liver ischemia-reperfusion injury by integrating bioinformatics and machine learning algorithms.

BACKGROUND: Liver ischemia-reperfusion injury (LIRI) is closely associated with immune infiltration,...

A systematic evaluation of Euclidean alignment with deep learning for EEG decoding.

Electroencephalography signals are frequently used for various Brain-Computer interface (BCI) tasks....

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images.

To develop a robust segmentation model, encoding the underlying features/structures of the input dat...

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images i...

Why your doctor is not an algorithm: Exploring logical principles of different clinical inference methods using liver transplantation as a model.

The development of machine learning (ML) tools in many different medical settings is largely increas...

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Determining the viability of a new drug molecule is a time- and resource-intensive task that makes c...

MedYOLO: A Medical Image Object Detection Framework.

Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical ...

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), howe...

Deep learning for 3D biliary anatomy for living liver donor hepatectomy planning.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. T...

Predicting kidney allograft survival with explainable machine learning.

INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allograft...

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplan...

CMAN: Cascaded Multi-scale Spatial Channel Attention-guided Network for large 3D deformable registration of liver CT images.

Deformable image registration is an essential component of medical image analysis and plays an irrep...

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Research on solid organ transplantation has taken advantage of the substantial acquisition of medica...

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim o...

Generalizability of deep learning in organ-at-risk segmentation: A transfer learning study in cervical brachytherapy.

PURPOSE: Deep learning can automate delineation in radiation therapy, reducing time and variability....

Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.

OBJECTIVE: We sought to develop Artificial Intelligence (AI) based models to predict non-transplanta...

Synergistic integration of deep learning with protein docking in cardiovascular disease treatment strategies.

This research delves into the exploration of the potential of tocopherol-based nanoemulsion as a the...

Editorial Comment: Usefulness of a Deep Learning Model for Pediatric Abdominal Organ Segmentation.

A roadmap for model-based bioprocess development.

The bioprocessing industry is undergoing a significant transformation in its approach to quality ass...

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia.

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to l...