1. Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects
- Author
-
Alexander Reiterer, Rodrigo Suarez-Ibarrola, Arkadiusz Miernik, Simon Hein, and Misgana Negassi
- Subjects
Urology ,02 engineering and technology ,Machine learning ,computer.software_genre ,Convolutional neural network ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Data acquisition ,Medical image analysis ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Image Processing, Computer-Assisted ,Humans ,Bladder cancer ,Artificial neural network ,medicine.diagnostic_test ,business.industry ,Deep learning ,Frame (networking) ,Cystoscopy ,medicine.disease ,Topic Paper ,Visualization ,Cystoscopic images ,030220 oncology & carcinogenesis ,020201 artificial intelligence & image processing ,Artificial intelligence ,Neural Networks, Computer ,business ,computer ,Neural networks ,Forecasting - Abstract
BackgroundOptimal 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.ObjectiveTo provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition.Evidence acquisitionA 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 synthesisIn 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.ConclusionAI-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.
- Published
- 2020