Does Artificial Intelligence Meaningfully Enhance Cystoscopy?
Menée à l'aide de 69 204 images cystoscopiques réalisées auprès de 10 729 patients, cette étude évalue la performance d'un outil diagnostique utilisant un algorithme d'apprentissage automatique pour détecter un cancer de la vessie
Cystoscopy, a procedure developed in the nineteenth century to visualize the inside of the urinary bladder, is a critical step in the management of patients suspected of having bladder cancer.1 Over the years, advances in optics and instrumentation have improved the fidelity of visualization of the urothelial lining. More recently, enhanced cystoscopic techniques such as narrow band imaging and blue light cystoscopy, have increased our ability to differentiate bladder tumors from adjacent normal tissue. Consequent prospective studies of these techniques have demonstrated improvements in cancer detection. Still, the experienced urologist’s eye, honed over thousands of procedures, is critical to interpret cystoscopic findings: multiple studies have shown that experience is independently associated with improved outcomes.
Journal of the National Cancer Institute , éditorial en libre accès, 2020