Transplantation

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

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Recurrent neural network with noise rejection for cyclic motion generation of robotic manipulators.

Recurrent neural network (RNN), as a kind of neural network with outstanding computing capability, i...

VR-Caps: A Virtual Environment for Capsule Endoscopy.

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastr...

Dynamically learned PSO based neighborhood influenced fuzzy c-means for pre-treatment and post-treatment organ segmentation from CT images.

BACKGROUND AND OBJECTIVE: The accurate segmentation of pre-treatment and post-treatment organs is al...

A survey on incorporating domain knowledge into deep learning for medical image analysis.

Although deep learning models like CNNs have achieved great success in medical image analysis, the s...

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, incl...

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection an...

Tacrolimus Exposure Prediction Using Machine Learning.

The aim of this work is to estimate the area-under the blood concentration curve of tacrolimus (TAC)...

Dynamics of Systemic Inflammation as a Function of Developmental Stage in Pediatric Acute Liver Failure.

The Pediatric Acute Liver Failure (PALF) study is a multicenter, observational cohort study of infan...

Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia.

Retrotransposons can cause somatic genome variation in the human nervous system, which is hypothesiz...

MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism.

BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepit...

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that...

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

IMPORTANCE: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate ...

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to imm...

Clinical Case Report: Dissociation of Clinical Course of Coexisting Autoimmune Hepatitis and Graves Disease.

OBJECTIVE: Concurrent autoimmune disorders, including autoimmune hepatitis (AIH), with Graves diseas...

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19)...

Prognostic Biomarkers for Thrombotic Microangiopathy after Acute Graft-versus-Host Disease: A Nested Case-Control Study.

Transplantation-associated thrombotic microangiopathy (TA-TMA) is a complication of allogeneic hemat...

High-resolution 3D abdominal segmentation with random patch network fusion.

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed to...

Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to...

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ ...

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy.

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed...

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