Erica On-Ting Chan1, Benjamin Pradere2, Jeremy Yuen-Chun Teoh1. 1. S.H. Ho Urology Centre, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China. 2. Department of Urology, Medical University of Vienna, Vienna, Austria.
Abstract
PURPOSE OF REVIEW: White light cystoscopy is the current standard for primary diagnosis and surveillance of bladder cancer. However, cancer changes can be subtle and may be easily missed. With the advancement of deep learning (DL), image recognition by artificial intelligence (AI) proves a high accuracy for image-based diagnosis. AI can be a solution to enhance bladder cancer diagnosis on cystoscopy. RECENT FINDINGS: An algorithm that classifies cystoscopic images into normal and tumour images is essential for AI cystoscopy. To develop this AI-based system requires a training dataset, an appropriate type of DL algorithm for the learning process and a specific outcome classification. A large data volume with minimal class imbalance, data accuracy and representativeness are pre-requisite for a good dataset. Algorithms developed during the past two years to detect bladder tumour achieved high performance with a pooled sensitivity of 89.7% and specificity of 96.1%. The area under the curve ranged from 0.960 to 0.980, and the accuracy ranged from 85.6 to 96.9%. There were also favourable results in the various attempts to enhance detection of flat lesions or carcinoma-in-situ. SUMMARY: AI cystoscopy is a possible solution in clinical practice to enhance bladder cancer diagnosis, improve tumour clearance during transurethral resection of bladder tumour and detect recurrent tumours upon surveillance.
PURPOSE OF REVIEW: White light cystoscopy is the current standard for primary diagnosis and surveillance of bladder cancer. However, cancer changes can be subtle and may be easily missed. With the advancement of deep learning (DL), image recognition by artificial intelligence (AI) proves a high accuracy for image-based diagnosis. AI can be a solution to enhance bladder cancer diagnosis on cystoscopy. RECENT FINDINGS: An algorithm that classifies cystoscopic images into normal and tumour images is essential for AI cystoscopy. To develop this AI-based system requires a training dataset, an appropriate type of DL algorithm for the learning process and a specific outcome classification. A large data volume with minimal class imbalance, data accuracy and representativeness are pre-requisite for a good dataset. Algorithms developed during the past two years to detect bladder tumour achieved high performance with a pooled sensitivity of 89.7% and specificity of 96.1%. The area under the curve ranged from 0.960 to 0.980, and the accuracy ranged from 85.6 to 96.9%. There were also favourable results in the various attempts to enhance detection of flat lesions or carcinoma-in-situ. SUMMARY: AI cystoscopy is a possible solution in clinical practice to enhance bladder cancer diagnosis, improve tumour clearance during transurethral resection of bladder tumour and detect recurrent tumours upon surveillance.
Authors: Jeong Woo Yoo; Kyo Chul Koo; Byung Ha Chung; Sang Yeop Baek; Su Jin Lee; Kyu Hong Park; Kwang Suk Lee Journal: Sci Rep Date: 2022-10-21 Impact factor: 4.996