Overview of current applications and trends in artificial intelligence for cystoscopy and transurethral resection of bladder tumours

Abstract

Purpose of review Accurate preoperative and intraoperative identification and complete resection of bladder cancer is essential. Adequate postoperative follow-up and observation are important to identify early intravesical recurrence or progression. However, the accuracy of diagnosis and treatment is dependent on the knowledge and experience of the physicians. Artificial intelligence (AI) can be an important tool for physicians performing cystoscopies. Recent findings Reports published over the past year and a half have identified an adequate amount of cystoscopy datasets for deep learning, with rich datasets of multiple tumour types including images of flat, carcinoma-in-situ, and elevated lesions, and more diverse applications. In addition to detecting bladder tumours, AI can assist in diagnosing interstitial cystitis. Applications of AI using conventional white-light and also to bladder endoscopy with different image enhancement techniques and manufacturers is underway. A framework has also been proposed to standardise the management of clinical data from cystoscopy to aid education and AI development and to compare with gastrointestinal endoscopic AI. Although real-world clinical applications have lagged, technological developments are progressing. Summary AI-based cystoscopy is likely to become an important tool and is expected to have real-world clinical applications comprehensively linking AI and imaging, data management systems, and clinicians. Video abstract http://links.lww.com/COU/A45