ReviewWorld journal of urology2020
Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects.
Review in World journal of urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- DeepBCTPred: deep learning-based prediction of bladder cancer tissues from endoscopic images.Bioinformatics advances · 2026Article
- Beyond Visualization: Advanced Imaging, Theragnostics and Biomarker Integration in Urothelial Bladder Cancer.Cancers · 2025Review
- The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects.Diagnostics (Basel, Switzerland) · 2025Review
- Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.Nature reviews. Urology · 2025Review
- Article
- Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment.Nature reviews. Urology · 2024Review
- Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications.International neurourology journal · 2023Article
- Education and training evolution in urolithiasis: A perspective from European School of Urology.Asian journal of urology · 2023Review
- Article
- A Comparative Study of Deep Neural Networks for Real-Time Semantic Segmentation during the Transurethral Resection of Bladder Tumors.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.Scientific reports · 2022Article
- Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.Tomography (Ann Arbor, Mich.) · 2022Article
- Explainable artificial intelligence (XAI): closing the gap between image analysis and navigation in complex invasive diagnostic procedures.World journal of urology · 2022Review
- [Use of medical archives for research and patient care].Der Urologe. Ausg. A · 2022Review
- Cultural and Creative Product Design and Image Recognition Based on the Convolutional Neural Network Model.Computational intelligence and neuroscience · 2022Article
- FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction.Frontiers in public health · 2022Article
- Transfer learning with fine-tuned deep CNN ResNet50 model for classifying COVID-19 from chest X-ray images.Informatics in medicine unlocked · 2022Article
- Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images.SN computer science · 2022Article
- On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays.Sensors (Basel, Switzerland) · 2021Article
- Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.International journal of imaging systems and technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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 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.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.