Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status a
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TOPIC PAPER
Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects Misgana Negassi1,2 · Rodrigo Suarez‑Ibarrola3 · Simon Hein3 · Arkadiusz Miernik3 · Alexander Reiterer1,2 Received: 25 July 2019 / Accepted: 13 December 2019 © The Author(s) 2020
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 Neural networks · Deep learning · Cystoscopic images · Medical image analysis * Alexander Reiterer [email protected] Misgana Negassi [email protected] Rodrigo Suarez‑Ibarrola rodrigo.suarez@uniklinik‑freiburg.de Simon Hein simon.hein@uniklinik‑freiburg.de Arkadiusz Miernik arkadiusz.miernik@uniklinik‑freiburg.de 1
Department of Sustainable Systems Engineering INATECH, University of Freiburg, Emmy‑Noether‑Straße 2, Freiburg, Germany
2
Department Object and Shape Detection, Fraunhofer Institute for Physical Measurement Techniques IPM, Heidenhofstraße 8, Freiburg, Germany
3
Department of Urology, Faculty of Medicine, University of Freiburg-Medical Centre, Hugstetter Str. 55, Freiburg, Germany
Introduction Bladder cancer (BCa) accounts for approximately 7% of all newly diagnosed cancers in the USA, being the fourth most common cancer and eighth most lethal in men [1, 2]. In Germany, BCa represents 4.7% of all new cancer cases and 3.2% of all cancer-related deaths [3]. Despite a low stage and grade, recurrence rates range from 50 to 75% at 20 years, setting patients at risk of progression to muscle-in
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