Nephrology

Recruiting Teacher IF Modality for Nephropathy Diagnosis: A Customized Distillation Method With Attention-Based Diffusion Network.

The joint use of multiple modalities for medical image processing has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for a lot of medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used multi-modality medical images due to its ease of acquisition and the effectiveness for...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients often cycle through cytotoxic chemotherapy (CC), epigenetic modifiers (EM) or small molecule inhibitors (SMI) empirically. Ideal therapy at each line remains unknown. We conducted a retrospective, multiple intervention, 'target-trial' using the PETAL global cohort. Patients received front-line CC, th...

Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of...

Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning.

Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared wi...

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associa...

Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.

Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive underst...

Comparative analysis of three chatbot responses on pediatric primary nocturnal enuresis.

BACKGROUND: The purpose of the study was to evaluate both the accuracy and reproducibility of the answers given by ChatGPT-4o®, Gemini® and Copilot® t...

Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-m...

A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.

BACKGROUND: Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinica...

Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors.

BACKGROUND: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to ...

Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians....

Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern.

BACKGROUND: Maintenance hemodialysis patients experience high morbidity and mortality, primarily from cardiovascular and infectious diseases. It was d...

Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.

BACKGROUND: Illicit kidney trade networks, operating globally, involve intricate interactions among various players, most notably buyers, sellers, bro...

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these ...

Green finance reform, multifaceted collaborative governance, and corporate greenwashing: Evidence from double/debiased machine learning method.

In order to promote the economic structure's green transformation and identify replicable and generalizable practices in green financial development, ...

Risk Prediction Models for Contrast-associated Acute Kidney Injury After Percutaneous Coronary Intervention: A Systematic Review.

The aim of this review was to systematically review published studies on risk prediction models for contrast-associated acute kidney injury (CA-AKI) i...

Machine learning for predicting medical outcomes associated with acute lithium poisoning.

The use of machine learning algorithms and artificial intelligence in medicine has attracted significant interest due to its ability to aid in predict...

A population based optimization of convolutional neural networks for chronic kidney disease prediction.

Chronic kidney disease (CKD) is a global public health concern, and the timely detection of the disease is priceless. Most of the classical machine le...

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic respons...

Evaluation of Perioperative Cardiovascular Event Risk in Gastrointestinal Surgery　- Predictive Modeling and Risk Stratification Using Machine Learning.

BACKGROUND: Preoperative risk assessment is very important to ensure surgical safety and predict postoperative complications. However, no large-scale ...