Urology

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

PURPOSE: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this view...

Artificial Intelligence versus Radiologists: A Comparative Analysis for Detecting Clinically Significant Prostate Cancer.

Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this stud...

Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to l...

On Entity Embeddings for Ordinal Features as Representation Learning in Recurrence Prediction of Urothelial Bladder Cancer.

BACKGROUND: Urothelial Bladder Cancer (UBC) is a common cancer with a high risk of recurrence, which is influenced by the TNM classification, grading,...

Optimizing ICU Care: Machine Learning and PCA for Early Prediction of Renal Replacement Therapy Requirement.

Forecasting the need for Renal Replacement Therapy (RRT) in intensive care units (ICUs) at an early stage can enhance patient outcomes and optimize re...

Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients.

Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability...

Bioimpedance a Diagnostic Tool for Tobacco Induced Oral Lesions: a Mixed Model cross-sectional study

Introduction: Electrical impedance spectroscopy (EIS) has recently developed as a novel diagnostic device for screening and evaluating cervical dysp...

Personalized graph feature-based multi-omics data integration for cancer subtype identification

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment ch...

Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites.

CCCTC-binding factor (CTCF) is an insulator protein that binds to a highly conserved DNA motif and facilitates regulation of three-dimensional (3D) nu...

Utilization of machine learning methods for prediction of acute and late rectal toxicity due to curative prostate radiotherapy.

OBJECTIVE: Rectal toxicity is one of the primary dose-limiting side effects of prostate cancer radiotherapy, and consequential impairment on quality o...

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularl...

Trends of "Artificial Intelligence, Machine Learning, Virtual Reality, and Radiomics in Urolithiasis" over the Last 30 Years (1994-2023) as Published in the Literature (PubMed): A Comprehensive Review.

To analyze the bibliometric publication trend on the application of "Artificial Intelligence (AI) and its subsets (Machine Learning-ML, Virtual reali...

Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.

Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience...

A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.

The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of...

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.

BACKGROUND: Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC...

A Neural Network-Based Scoring System for Predicting Prognosis and Therapy in Breast Cancer.

Breast cancer is a prevalent malignancy affecting women worldwide. Currently, there are no precise molecular biomarkers with immense potential for acc...

Advancements in artificial intelligence for robotic-assisted radical prostatectomy in men suffering from prostate cancer: results from a scoping review.

BACKGROUND: Robotic-assisted radical prostatectomy (RARP) is currently a first-line treatment option for men with localized prostate cancer (PCa), at ...

[Research Progress of Artificial Intelligence in Prostate Cancer Diagnosis Application].

With the continuous advancement of artificial intelligence in the field of prostate cancer research, numerous studies have shown that AI performance c...

Integrated multi-omics with machine learning to uncover the intricacies of kidney disease.

The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensi...