Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management
Carlo Gandi, Luigi Vaccarella, Riccardo Bientinesi, Marco Racioppi, Francesco Pierconti, Emilio Sacco
Abstract
Machine learning (ML) is the subfield of artificial intelligence (AI), born from the marriage between statistics and computer science, with the unique purpose of building prediction algorithms able to improve their performances by automatically learning from massive data sets. The availability of ever-growing computational power and highly evolved pattern recognition software has led to the spread of ML-based systems able to perform complex tasks in bioinformatics, medical imaging, and diagnostics. These intelligent tools could be the answer to the unmet need for non-invasive and patient-tailored instruments for the diagnosis and management of bladder cancer (BC), which are still based on old technologies and unchanged nomograms. We reviewed the most significant evidence on ML in the diagnosis, prognosis, and management of bladder cancer, to find out if these intelligent technologies are ready to be introduced into the daily clinical practice of the urologist.