Chan Erica On-Ting, Pradere Benjamin, Teoh Jeremy Yuen-Chun
S.H. Ho Urology Centre, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.
Department of Urology, Medical University of Vienna, Vienna, Austria.
Curr Opin Urol. 2021 Jul 1;31(4):397-403. doi: 10.1097/MOU.0000000000000900.
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.
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.
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.
白光膀胱镜检查是目前膀胱癌初步诊断和监测的标准方法。然而，癌症变化可能很细微，容易被遗漏。随着深度学习（DL）的发展，人工智能（AI）的图像识别在基于图像的诊断中显示出很高的准确性。人工智能可以作为一种提高膀胱镜检查中膀胱癌诊断的方法。
一种将膀胱镜图像分类为正常图像和肿瘤图像的算法对于人工智能膀胱镜检查至关重要。要开发这种基于人工智能的系统，需要一个训练数据集、一种适合学习过程的深度学习算法类型以及特定的结果分类。一个大数据量、最小类不平衡、数据准确性和代表性是一个好数据集的先决条件。在过去两年中开发的用于检测膀胱肿瘤的算法取得了高性能，汇总灵敏度为89.7%，特异性为96.1%。曲线下面积范围为0.960至0.980，准确率范围为85.6至96.9%。在各种增强扁平病变或原位癌检测的尝试中也取得了良好的结果。
人工智能膀胱镜检查是临床实践中提高膀胱癌诊断、改善经尿道膀胱肿瘤切除术中肿瘤清除率以及在监测时检测复发肿瘤的一种可能解决方案。
