Transplantation

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

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A machine learning artefact detection method for single-channel infant event-related potential studies.

. Automated detection of artefact in stimulus-evoked electroencephalographic (EEG) data recorded in ...

Hierarchical multi-task deep learning-assisted construction of human gut microbiota reactive oxygen species-scavenging enzymes database.

In the process of oxygen reduction, reactive oxygen species (ROS) are generated as intermediates, in...

Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.

Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy...

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

BACKGROUND: Diagnosis of kidney transplant rejection currently relies on manual histopathological as...

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary.

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor...

A flexible, stretchable and wearable strain sensor based on physical eutectogels for deep learning-assisted motion identification.

Physical eutectogels as a newly emerging type of conductive gel have gained extensive interest for t...

Robot-assisted system for non-invasive wide-range flexible eye positioning and tracking in particle radiotherapy.

Particle (proton, carbon ion, or others) radiotherapy for ocular tumors is highly dependent on preci...

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides wit...

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocel...

When an extra rejection class meets out-of-distribution detection in long-tailed image classification.

Detecting Out-of-Distribution (OOD) inputs is essential for reliable deep learning in the open world...

Predicting Success in Descemet Membrane Endothelial Keratoplasty Using Machine Learning.

PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet mem...

A Machine Learning Framework for Screening Plasma Cell-Associated Feature Genes to Estimate Osteoporosis Risk and Treatment Vulnerability.

Osteoporosis, in which bones become fragile owing to low bone density and impaired bone mass, is a g...

The emerging role of generative artificial intelligence in transplant medicine.

Generative artificial intelligence (AI), a subset of machine learning that creates new content based...

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiati...

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...

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