Urology

Simulated arbitration of discordance between radiologists and artificial intelligence interpretation of breast cancer screening mammograms.

Artificial intelligence (AI) algorithms have been retrospectively evaluated as replacement for one radiologist in screening mammography double-reading; however, methods for resolving discordance between radiologists and AI in the absence of 'real-world' arbitration may underestimate cancer detection rate (CDR) and recall. In 108,970 consecutive screens from a population screening program (BreastSc...

Predictability of varicocele repair success: preliminary results of a machine learning-based approach.

Varicocele is a prevalent condition in the infertile male population. However, to date, which patients may benefit most from varicocele repair is still a matter of debate. The purpose of this study was to evaluate whether certain preintervention sperm parameters are predictive of successful varicocele repair, defined as an improvement in total motile sperm count (TMSC). We performed a retrospectiv...

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radi...

High-Throughput Screening and Prediction of Nucleophilicity of Amines Using Machine Learning and DFT Calculations.

Nucleophilic index () as a significant parameter plays a crucial role in screening of amine catalysts. Indeed, the quantity and variety of amines are ...

The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on F-PSMA-1007 PET.

PURPOSE: Convolutional Neural Networks (CNNs) have emerged as transformative tools in the field of radiation oncology, significantly advancing the pre...

Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy.

BACKGROUND: Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely A...

Ensemble of machine learning techniques to predict survival in kidney transplant recipients.

Kidney transplant recipients face a high cardiovascular risk, which is a leading cause of death in this patient group. This article proposes the appli...

The Potential Impact of Large Language Models on Doctor-Patient Communication: A Case Study in Prostate Cancer.

BACKGROUND: In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doct...

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images.

There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-bas...

Recent trends in AI applications for pelvic MRI: a comprehensive review.

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum....

Automated segmentation in pelvic radiotherapy: A comprehensive evaluation of ATLAS-, machine learning-, and deep learning-based models.

Artificial intelligence can standardize and automatize highly demanding procedures, such as manual segmentation, especially in an anatomical site as c...

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation.

BACKGROUND: Oxidative stress is an unavoidable process in kidney transplantation and is closely related to the development of acute rejection after ki...

A commentary on 'A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study'.

Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T-cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine learning: bioinformatics multi-omics study.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are found in primary and advanced tumours. They are primarily involved in tumour progression through ...

Artificial intelligence and machine learning applications in urinary tract infections identification and prediction: a systematic review and meta-analysis.

BACKGROUND: Urinary tract infections (UTIs) have been one of the most common bacterial infections in clinical practice worldwide. Artificial intellige...

A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.

: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to...

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

BACKGROUND: With the development of artificial intelligence, the application of machine learning to develop predictive models for sepsis-associated ac...

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI)...

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.

PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year m...

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research effort...