Nephrology

GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights

Glomerular pathology is central to the diagnosis and prognosis of renal diseases, yet the heterogeneity of glomerular morphology and fine-grained lesion patterns remain challenging for current AI approaches. We present GloPath, an entity-centric foundation model trained on over one million glomeruli extracted from 14,049 renal biopsy specimens using multi-scale and multi-view self-supervised learn...

Explainable AI for end-to-end pathogen target discovery and molecular design

Drug discovery is often constrained by target identification, a bottleneck especially acute in antimicrobial development and the fight against emerging fungicide resistance. We present APEX (Attention-based Protein EXplainer), an explainable AI framework for cross-species, proteome-scale target discovery and pocket-guided molecular design. APEX combines ESM-2 evolutionary embeddings, graph attenti...

BiM-GeoAttn-Net: Linear-Time Depth Modeling with Geometry-Aware Attention for 3D Aortic Dissection CTA Segmentation

Accurate segmentation of aortic dissection (AD) lumens in CT angiography (CTA) is essential for quantitative morphological assessment and clinical dec...

CT-based Automated Volumetry as a Biomarker of Global and Split Renal Function in Living Kidney Donors

Background: Kidney volumetry derived from CT has been proposed as a surrogate of renal function in living kidney donor evaluation. However, clinical i...

Machine learning-based prediction of cardiovascular disease risk in Africa using WHO Stepwise Surveys: 2014-2019

Introduction: Cardiovascular diseases (CVDs) are the leading cause of death globally, with rising burdens in Africa due to ageing populations, lifesty...

A Data-Driven Approach to Support Clinical Renal Replacement Therapy

This study investigates a data-driven machine learning approach to predict membrane fouling in critically ill patients undergoing Continuous Renal Rep...

Enhancing Renal Tumor Malignancy Prediction: Deep Learning with Automatic 3D CT Organ Focused Attention

Accurate prediction of malignancy in renal tumors is crucial for informing clinical decisions and optimizing treatment strategies. However, existing i...

Large-Language Models for data extraction from written kidney biopsy reports

Introduction: Kidney biopsy reports contain rich information that is clinically actionable and useful for research. However, the narrative format hind...

Inference of cancer driver mutations from tumor microenvironmentcomposition: a pan-cancer study with cross-platform external validation

Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone can predict genotype has not been systematically eva...

AI-DRIVEN DIAGNOSIS OF NON-ALCOHOLIC FATTY LIVER DISEASE AND ASSOCIATED COMORBIDITIES

Non-alcoholic fatty liver disease (NAFLD) is a globally prevalent hepatic condition caused by the buildup of fat in the liver. It is frequently associ...

Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit

Machine learning adoption in clinical decision support systems remains limited by concerns about transparency and robustness. Causal structure learnin...

Comparing Modelling Architectures in the context of EGFR Status Classification in Non Small Cell Lung Cancer

Radiogenomics enables the non invasive characterisation of the genomic and molecular properties of tumours, with epidermal growth factor receptor (EGF...

Decoding the metabolic blockade effect: PFAS inhibition of organic anion transporters impairs VOC clearance and amplifies neurocognitive decline

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicol...

High-Resolution 3D Histology of the Murine Kidney Using Synchrotron X-Ray Micro-CT

Conventional two-dimensional (2D) histology relies upon destructive sample preparation and stereological estimation, frequently leading to sampling bi...

UCSF RMaC: University of California San Francisco 3D Multi-Phase Renal Mass CT Dataset with Tumor Segmentations

Current standard of care imaging practices cannot reliably differentiate among certain renal tumors such as benign oncocytoma and clear cell renal cel...

Identifying Reasons for ACEI/ARB Non-Use in CKD Using Scalable Clinical NLP with Schema-Guided LLM Augmentation

IMPORTANCE: Although angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic...

AMAP-APP: Efficient Segmentation and Morphometry Quantification of Fluorescent Microscopy Images of Podocytes

Background: Automated podocyte foot process quantification is vital for kidney research, but the established "Automatic Morphological Analysis of Podo...

LLM-based reconstruction of longitudinal clinical trajectories in chronic liver disease.

Background: Liver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor ...

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression ...

Decoding Future Risk: Deep Learning Analysis of Tubular Adenoma Whole-Slide Images

Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aim...