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.

Evidence acquisition

A detailed search of original articles was performed using the PubMed-MEDLINE database to identify recent English literature relevant to ML, DL and CNN applications in cystoscopic image recognition.

Evidence synthesis

In total, two articles and one conference abstract were identified addressing the application of AI methods in cystoscopic image recognition. These investigations showed accuracies exceeding 90% for tumor detection; however, future work is necessary to incorporate these methods into AI-aided cystoscopy and compared to other tumor visualization tools. Furthermore, we present results from the RaVeNNA-4pi consortium initiative which has extracted 4200 frames from 62 videos, analyzed them with the U-Net network and achieved an average dice score of 0.67. Improvements in its precision can be achieved by augmenting the video/frame database.

Conclusion

AI-aided cystoscopy has the potential to outperform urologists at recognizing and classifying bladder lesions. To ensure their real-life implementation, however, these algorithms require external validation to generalize their results across other data sets.

中文翻译：

---

人工神经网络在膀胱镜图像自动分析中的应用：现状回顾和未来前景。

背景

膀胱癌 (BCa) 的最佳检测和监测主要依赖于膀胱病变的膀胱镜可视化。人工智能辅助膀胱镜检查可以改善图像识别并加速数据采集。

客观的

全面回顾机器学习 (ML)、深度学习 (DL) 和卷积神经网络 (CNN) 在膀胱镜图像识别中的应用。

证据获取

使用 PubMed-MEDLINE 数据库对原始文章进行了详细搜索，以识别与膀胱镜图像识别中的 ML、DL 和 CNN 应用相关的最新英文文献。

证据综合

总共有两篇文章和一篇会议摘要讨论了人工智能方法在膀胱镜图像识别中的应用。这些研究表明肿瘤检测的准确率超过 90%；然而，未来的工作有必要将这些方法纳入人工智能辅助膀胱镜检查中，并与其他肿瘤可视化工具进行比较。此外，我们还展示了 RaVeNNA-4pi 联盟计划的结果，该计划从 62 个视频中提取了 4200 帧，使用 U-Net 网络对其进行分析，并获得了 0.67 的平均骰子分数。通过增加视频/帧数据库可以提高其精度。

结论

人工智能辅助膀胱镜检查在识别和分类膀胱病变方面有可能超越泌尿科医生。然而，为了确保它们在现实生活中的实现，这些算法需要外部验证以将其结果推广到其他数据集。