Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects

World J Urol. 2020 Oct;38(10):2349-2358. doi: 10.1007/s00345-019-03059-0. Epub 2020 Jan 10.

Abstract

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.

Keywords: Cystoscopic images; Deep learning; Medical image analysis; Neural networks.

Publication types

  • Review

MeSH terms

  • Cystoscopy*
  • Forecasting
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Image Processing, Computer-Assisted / trends
  • Machine Learning
  • Neural Networks, Computer*