Nephrology

GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs

Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-level classification, our proposed method, named GraphMorph, focuses on branch-level features of tubular structures to achieve more topologically accurate predictions. GraphMorph co...

Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires considerable expertise, time, and effort. Using a 2-stage classification strategy, we sought to examine the effectiveness of deep learning in detecting...

Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine lea...

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level

Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney bi...

Choroidal image analysis for OCT image sequences with applications in systemic health

The choroid, a highly vascular layer behind the retina, is an extension of the central nervous system and has parallels with the renal cortex, with ...

Proprioceptive Origami Manipulator

Origami offers a versatile framework for designing morphable structures and soft robots by exploiting the geometry of folds. Tubular origami structu...

Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors

Neural networks have become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates t...

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg

Accurate fine-grained segmentation of the renal vasculature is critical for nephrological analysis, yet it faces challenges due to diverse and insuf...

Differentiating between renal medullary and clear cell renal carcinoma with a machine learning radiomics approach.

BACKGROUND: The objective of this study was to develop and validate a radiomics-based machine learning (ML) model to differentiate between renal medul...

Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors

Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment pla...

INST-Sculpt: Interactive Stroke-based Neural SDF Sculpting

Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry, achieving impressive results in tasks s...

Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care

Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteri...

Application of artificial intelligence and machine learning for risk stratification acute kidney injury among hematopoietic stem cell transplantation patients: PCRRT ICONIC AI Initiative Group Meeting Proceedings.

Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased ris...

Enhancing Olfactory Perception Through Large Language Models: Integrating Sensory Data for Advanced Odor Recognition

The integration of biological principles into artificial olfactory systems has led to significant advancements in odor detection and classification....

Development and Validation of a Dynamic Kidney Failure Prediction Model based on Deep Learning: A Real-World Study with External Validation

Background: Chronic kidney disease (CKD), a progressive disease with high morbidity and mortality, has become a significant global public health pro...

[Predicting Intensive Care Unit Mortality in Patients With Heart Failure Combined With Acute Kidney Injury Using an Interpretable Machine Learning Model: A Retrospective Cohort Study].

OBJECTIVE: Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predict...

PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit

Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. Howe...

Prediction of Binding Affinity for ErbB Inhibitors Using Deep Neural Network Model with Morgan Fingerprints as Features

The ErbB receptor family, including EGFR and HER2, plays a crucial role in cell growth and survival and is associated with the progression of variou...

Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data

Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial ste...

Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This ...