Urology

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is time-consuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a gen...

Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analysis of the key parameters of urinary tract ultrasound in children with unilateral hydronephrosis. For children with unilateral hydronephrosis, DRF <40% serves as a compelling indication for surgical intervention, and it can be assessed through diureti...

SNER: Semi-Supervised Named Entity Recognition for Large Volume of Diabetes Data.

The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from thes...

Deep learning-based intraoperative visual guidance model for ureter identification in laparoscopic sigmoidectomy.

BACKGROUND: Identifying the left ureter is a key step while performing laparoscopic sigmoid resection to prevent intraoperative injury and postoperati...

Impact of artificial intelligence assisted lesion detection on radiologists' interpretation at multiparametric prostate MRI.

PURPOSE: To compare prostate cancer lesion detection using conventional and artificial intelligence (AI)-assisted image interpretation at multiparamet...

A dynamic model using k-NN algorithm for predicting diabetes and breast cancer.

Healthcare remains a critical focus due to its direct impact on human well-being. Diabetes, currently the fastest-growing chronic disease globally, po...

Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning.

Coronary artery disease (CAD) commonly occurs and elevates the risk of cardiovascular events and mortality in chronic kidney disease (CKD) patients. T...

Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.

PURPOSE OF REVIEW: The objective of this review is to provide an update on the application of artificial intelligence (AI) for the histological interp...

Multi-level feature fusion network for kidney disease detection.

Kidney irregularities pose a significant public health challenge, often leading to severe complications, yet the limited availability of nephrologists...

Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especial...

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques.

Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This s...

Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. T...

Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model.

BACKGROUND: Acute kidney injury (AKI) is a critical complication in intensive care units (ICUs) that is known to have multifaceted impacts. However, a...

An explainable adaptive channel weighting-based deep convolutional neural network for classifying renal disorders in computed tomography images.

Renal disorders are a significant public health concern and a cause of mortality related to renal failure. Manual diagnosis is subjective, labor-inten...

A deployment safety case for AI-assisted prostate cancer diagnosis.

Deep learning (DL) has the potential to deliver significant clinical benefits. In recent years, an increasing number of DL-based systems have been app...

MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models

The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasoun...

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupe...

A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke

Length of stay (LOS) prediction in acute stroke is critical for improving care planning. Existing machine learning models have shown suboptimal pred...

Identification of key genes regulating colorectal cancer stem cell characteristics by bioinformatics analysis.

Cancer stem cells (CSCs), distinguished by their abilities to differentiate and self-renew, play a pivotal role in the progression of colorectal cance...

Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.

BACKGROUND: Macrophages are closely associated with the progression of Kidney renal clear cell carcinoma (KIRC) and can influence programmed cell deat...