Artificial intelligence diagnostics for bladder tumor identification and grade prediction depend on narrow band imaging cystoscopy.
Authors
Affiliations (5)
Affiliations (5)
- Department of Urology, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China.
- Department of Urology, Qilu Hospital of Shandong University (Qingdao), Qingdao, China.
- Department of Urology, Rizhao Central Hospital, Rizhao, China.
- Albert Einstein College of Medicine - Jacobi Medical Center, New York, NY, USA.
- MsunHealth Technology Group Co., Ltd., No. 1237 Yingxiu Road, Jinan, Shandong 250101, China.
Abstract
The effective treatment of bladder cancer depends on early evaluation through cystoscopy. Given the clinical importance of distinguishing the tumor grade, we report the application of the AI-assisted NBI Cystoscopy Diagnostic System (AINCDS). The AINCDS consists of (1) dual-channel feature extraction module, (2) lesion segmentation module based on feature pyramids, and (3) a multi-task classification module. AINCDS achieved an accuracy for identifying bladder cancer of 0.919 (95% CI = 0.896 to 0.938). For the prediction of tumor grade, the accuracy was 0.764 (95% CI = 0.714 to 0.810). The AINCDS demonstrates similar ability comparable to urologists with over 10 years' experience. With the assistance of AINCDS, the tumor grade prediction accuracy of urologists with 1-3 years' experience improved from 0.667 to 0.793. AINCDS can assist in the diagnosis of bladder cancer and prediction of tumor grade, offering the potential to improve the accuracy of lesion assessment and reduce the workload of urologists.