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Application of artificial neural networks for automated analysis of cystoscopic images

A review of the current status and future prospects
: Negassi, Misgana; Suarez-Ibarrola, Rodrigo; Hein, Simon; Miernik, Arkadiusz; Reiterer, Alexander

Volltext ()

World journal of urology 38 (2020), Nr.10, S.2349-2358
ISSN: 0724-4983
ISSN: 1433-8726
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
13GW0203A; RaVeNNA-4pi
Digitale Plattform mit 4PI-real-time-Endoimaging zur endoskopischen 3D-Rekonstruktion, Visualisierung und Nachsorgeunterstützung von Patienten mit Harnblasenkarzinom
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IPM ()
neural network; deep learning; Cystoscopic Images; Medical Image Analysis

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.