A novel endoimaging system for endoscopic 3D reconstruction in bladder cancer patients.

Introduction: The methods employed to document cystoscopic findings in bladder cancer patients lack accuracy and are subject to observer variability. We propose a novel endoimaging system and an online documentation platform to provide post-procedural 3D bladder reconstructions for improved diagnosis, management and follow-up.

Material And Methods: The RaVeNNA4pi consortium is comprised of five industrial partners, two university hospitals and two technical institutes.

Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects.

Background: Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition.

Objective: To provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition.