Urology

Deep learning identifies histopathologic changes in bladder cancers associated with smoke exposure status.

Smoke exposure is associated with bladder cancer (BC). However, little is known about whether the histologic changes of BC can predict the status of smoke exposure. Given this knowledge gap, the current study investigated the potential association between histology images and smoke exposure status. A total of 483 whole-slide histology images of 285 unique cases of BC were available from multiple c...

Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

PURPOSE: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and orga...

Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

Ammonia (NH) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH is essential for early screening of kidne...

Using machine learning to predict the risk of short-term and long-term death in acute kidney injury patients after commencing CRRT.

BACKGROUND: The mortality rate and prognosis of short-term and long-term acute kidney injury (AKI) patients who undergo continuous renal replacement t...

Quality of Information About Kidney Stones from Artificial Intelligence Chatbots.

Kidney stones are common and morbid conditions in the general population with a rising incidence globally. Previous studies show substantial limitati...

Using artificial intelligence to generate medical literature for urology patients: a comparison of three different large language models.

PURPOSE: Large language models (LLMs) are a form of artificial intelligence (AI) that uses deep learning techniques to understand, summarize and gener...

Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models.

The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinic...

Comparison of data fusion strategies for automated prostate lesion detection using mpMRI correlated with whole mount histology.

BACKGROUND: In this work, we compare input level, feature level and decision level data fusion techniques for automatic detection of clinically signif...

Prediction of post-donation renal function using machine learning techniques and conventional regression models in living kidney donors.

BACKGROUND: Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney don...

Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer.

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and e...

Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function ...

Artificial intelligence in chronic kidney diseases: methodology and potential applications.

Chronic kidney disease (CKD) represents a significant global health challenge, characterized by kidney damage and decreased function. Its prevalence h...

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.

INTRODUCTION: Sepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-bas...

[Large language models in science].

OBJECTIVE: Large language models (LLMs) are gaining popularity due to their ability to communicate in a human-like manner. Their potential for science...

A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.

OBJECTIVES: The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learn...

Using ensemble learning and hierarchical strategy to predict the outcomes of ESWL for upper ureteral stone treatment.

Urinary tract stones are a common and frequently recurring medical issue. Accurately predicting the success rate after surgery can help avoid ineffect...

Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice.

The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the patholo...

Improved quantitative parameter estimation for prostate T relaxometry using convolutional neural networks.

OBJECTIVE: Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image serie...

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can ...

Development and Validation of a Biparametric MRI Deep Learning Radiomics Model with Clinical Characteristics for Predicting Perineural Invasion in Patients with Prostate Cancer.

RATIONALE AND OBJECTIVES: Perineural invasion (PNI) is an important prognostic biomarker for prostate cancer (PCa). This study aimed to develop and va...