Transplantation

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and i...

Segmentation and classification of brain tumor using Taylor fire hawk optimization enabled deep learning approach.

The brain is a crucial organ that controls the body's neural system. The tumor develops and spreads across the brain as a result of irregular cell generation. The provision of substantial treatment to patients requires the early diagnosis of malignancies. However, timely diagnosis and accurate classification were difficult in the conventional models. Thus, the Taylor Fire Hawk optimization (TFHO) ...

Automated real-world data integration improves cancer outcome prediction.

The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcom...

AI for biofabrication.

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticthree-dimensional human organs. Such technology wo...

[Pathology of transplanted heart rejection using artificial intelligence-based image analysis of endomyocardial biopsies].

Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted ki...

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clea...

Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

BACKGROUND: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success ...

Using machine learning approaches to develop a fast and easy-to-perform diagnostic tool for patients with light chain amyloidosis: a retrospective real-world study.

Immunoglobulin light chain (AL) amyloidosis is a severe disorder caused by the accumulation of amyloid fibrils, leading to organ failure. Early diagno...

Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.

BACKGROUND: The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on...

Machine learning-based new classification for immune infiltration of gliomas.

BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of th...

A Bio-Inspired Magnetic Soft Robotic Fish for Efficient Solar-Energy Driven Water Purification.

Solar-driven water evaporation is a promising solution for global water scarcity but is still facing challenges due to its substantial energy requirem...

Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emi...

A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patie...

Improved survival prediction for kidney transplant outcomes using artificial intelligence-based models: development of the UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool.

The UK Deceased Donor Kidney Transplant Outcome Prediction (UK-DTOP) Tool, developed using advanced artificial intelligence (AI), significantly enhanc...

Electrocardiogram and respiration recordings show a reduction in the physical burden on professional caregivers when performing care tasks with a transfer support robot.

In this study, we assessed the physical burden on professional caregivers when using a transfer support robot, "Hug," to transfer and move a care reci...

A machine learning based framework to tailor properties of nanofiltration and reverse osmosis membranes for targeted removal of organic micropollutants.

Nanofiltration (NF) and reverse osmosis (RO) membranes play an increasingly important role in the removal of organic micropollutants (OMPs), which put...

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders ...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining t...

Open-set long-tailed recognition via orthogonal prototype learning and false rejection correction.

Learning from data with long-tailed and open-ended distributions is highly challenging. In this work, we propose OLPR, which is a new dual-stream Open...