Nephrology

Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods

Artificial intelligence models in healthcare often fail to improve patient outcomes despite strong predictive performance because they are frequently developed with limited understanding of clinical workflows and system implementation. We demonstrate a human-centered design approach to define prediction targets before model development, ensuring alignment with actionable clinical interventions. Us...

GARD: Genomic Data based Drug Repurposing in Head and Neck Cancer with Large Language Model Validation

Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surgery, radiation, and immunotherapy, have shown limited improvement in outcomes. Drug repurposing offers a cost-effective strategy to identify new therapeutic options by leveraging existing medications with known safety profiles. Within this study we dev...

Mass spectrometry and machine learning for classification and molecular phenotyping of renal cell carcinoma and benign tumors

Pathology classification of cancer tumor subtypes and benign tumors can be difficult due to cellular heterogeneity and similar morphological features....

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively...

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs)...

Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerul...

The accuracy of Machine learning in the prediction and diagnosis of diabetic kidney Disease: A systematic review and Meta-Analysis.

PURPOSE: Machine learning (ML) has gained attention in diabetes management, particularly for predicting and diagnosing diabetic kidney disease (DKD). ...

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of i...

Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach.

Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detec...

A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal funct...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop...

MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)

Accurate prediction of recurrence in clear cell renal cell carcinoma (ccRCC) remains a major clinical challenge due to the disease complex molecular...

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset

Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but th...

Airway Segmentation Network for Enhanced Tubular Feature Extraction

Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is...

Novel composite health assessment risk model for older allogeneic transplant recipients: BMT-CTN 1704.

Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for older adults with hematologic malignancies. Concerns on nonrelaps...

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, w...

PASC-Net:Plug-and-play Shape Self-learning Convolutions Network with Hierarchical Topology Constraints for Vessel Segmentation

Accurate vessel segmentation is crucial to assist in clinical diagnosis by medical experts. However, the intricate tree-like tubular structure of ...

Artificial intelligence in drug discovery: A comprehensive review with a case study on hyperuricemia, gout arthritis, and hyperuricemic nephropathy

This paper systematically reviews recent advances in artificial intelligence (AI), with a particular focus on machine learning (ML), across the enti...

TubuleTracker: a high-fidelity shareware software to quantify angiogenesis architecture and maturity

Background: In vitro endothelial cell culture is widely used to study angiogenesis. Histomicrographic images of cell networks are often analyzed man...

Topology-Constrained Learning for Efficient Laparoscopic Liver Landmark Detection

Liver landmarks provide crucial anatomical guidance to the surgeon during laparoscopic liver surgery to minimize surgical risk. However, the tubular...