A clinically validated AI framework for kidney cancer detection and characterization.

ABSTRACT:

Background

Renal cell carcinoma is one of the most common cancers of the urinary tract and is usually diagnosed by interpreting contrast-enhanced computed tomography scans. Rising demand for radiological services, combined with a shortage of radiologists, makes timely and accurate diagnosis increasingly challenging. Automated approaches may help radiologists improve efficiency and accuracy.

Methods

We developed BMVision, a deep learning-based tool for detecting and characterizing kidney cancer. The tool integrates with a web-based viewer designed to provide an intuitive interface for radiologists. Its performance was evaluated in a two-stage retrospective reader study. Six radiologists independently reviewed 200 scans across both AI-assisted and unaided workflows, allowing c

SUBMITTER: Petryshak B 

PROVIDER: S-EPMC12749715 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature