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

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Abstract

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. 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 comparison of diagnostic performance and workflow efficiency with and without support from the tool. Statistical analysis compared AI-aided and unaided workflows across predefined clinical criteria, including diagnostic sensitivity, lesion measurement, reporting efficiency, and inter-radiologist agreement, using non-parametric tests and bootstrapping. Here we show that BMVision reduces radiologists' reporting time by an average of 33%, up to 52%. The tool provides structured auto-generated reports, minimizing the need for manual dictation or typing. In addition, BMVision improves sensitivity for detecting benign renal lesions (from 79.9 to 86.3%) and leads to a significant increase in inter-radiologist agreement. To the best of our knowledge, BMVision is the first clinically validated commercial artificial intelligence tool for kidney cancer detection and characterization. By improving diagnostic accuracy and reporting efficiency, it has the potential to enhance patient care and help radiologists meet the growing demand for high-quality cancer diagnostics.

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