Bladder cancer (BCa) is the most common malignancy of the urinary tract and the most
expensive malignancy to treat over the patients' lifetime. In recent years a number of studies …

Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in the
diagnosis and treatment of bladder cancer, offering significant advancements in accuracy …

Simple Summary The diagnosis and prediction of prognosis for bladder cancer (BC) can be
challenging because of the subjective nature of pathological evaluation. Artificial …

Artificial intelligence is highly regarded as the most promising future technology that will
have a great impact on healthcare across all specialties. Its subsets, machine learning, deep …

Advances in deep learning and neural networking have allowed clinicians to understand the
impact that artificial intelligence (AI) could have on improving clinical outcomes and …

INTRODUCTION: As we enter the era of" big data," an increasing amount of complex health-
care data will become available. These data are often redundant," noisy," and characterized …

Purpose When exploring survival outcomes for patients with bladder cancer, most studies
rely on conventional statistical methods such as proportional hazards models. Given the …

Purpose: New techniques for the prediction of tumor behavior are needed, because
statistical analysis has a poor accuracy and is not applicable to the individual. Artificial …

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with
lymph node (LN) metastasis based on digital evaluation of primary tumors and train the …

Bladder cancer has been increasing globally. Urinary cytology is considered a major
screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize …