Nephrology

Autoencoder techniques for survival analysis on renal cell carcinoma.

Survival is the gold standard in oncology when determining the real impact of therapies in patients outcome. Thus, identifying molecular predictors of survival (like genetic alterations or transcriptomic patterns of gene expression) is one of the most relevant fields in current research. Statistical methods and metrics to analyze time-to-event data are crucial in understanding disease progression ...

Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model.

BACKGROUND: While liver transplantation saves lives from irreversible liver damage, it poses challenges such as graft dysfunction due to factors like ischemia-reperfusion (IR) injury, which can lead to significant cellular damage and systemic complications. Current diagnostic tools for detecting IR injury have limitations, necessitating advanced methods for timely intervention. This study explores...

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of ...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and M...

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for ...

Prediction of Cisplatin-Induced Acute Kidney Injury Using an Interpretable Machine Learning Model and Electronic Medical Record Information.

Predicting cisplatin-induced acute kidney injury (Cis-AKI) before its onset is important. We aimed to develop a predictive model for Cis-AKI using pat...

Artificial Intelligence to Predict Chronic Kidney Disease Progression to Kidney Failure: A Narrative Review.

Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficu...

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

BACKGROUND: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as t...

StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the signifi...

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.

INTRODUCTION: Accurate identification of graft loss in Electronic Medical Records of kidney transplant recipients is essential but challenging due to ...

Formalization of Biological Circuit Block Diagrams for formally analyzing Biomedical Control Systems in pHRI Applications

The control of Biomedical Systems in Physical Human-Robot Interaction (pHRI) plays a pivotal role in achieving the desired behavior by ensuring the ...

[Identification of kidney stone types by deep learning integrated with radiomics features].

Currently, the types of kidney stones before surgery are mainly identified by human beings, which directly leads to the problems of low classification...

A CNN Approach to Polygenic Risk Prediction of Kidney Stone Formation

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide A...

MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection

Problem: Pancreas radiological imaging is challenging due to the small size, blurred boundaries, and variability of shape and position of the organ ...

Survival analysis of clear cell renal cell carcinoma based on radiomics and deep learning features from CT images.

PURPOSE: To create a nomogram for accurate prognosis of patients with clear cell renal cell carcinoma (ccRCC) based on computed tomography images.

Predicting Survival of Hemodialysis Patients using Federated Learning

Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival ...

A Novel Ensemble-Based Deep Learning Model with Explainable AI for Accurate Kidney Disease Diagnosis

Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function, leading t...

A large language model-based approach to quantifying the effects of social determinants in liver transplant decisions

Patient life circumstances, including social determinants of health (SDOH), shape both health outcomes and care access, contributing to persistent d...

A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction

Purpose: A reliable cancer prognosis model for clear cell renal cell carcinoma (ccRCC) can enhance personalized treatment. We developed a multi-moda...

Novel machine learning technique further clarifies unrelated donor selection to optimize transplantation outcomes.

We investigated the impact of donor characteristics on outcomes in allogeneic hematopoietic cell transplantation (HCT) recipients using a novel machin...