ArticleScientific reports2022
Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 46 citations in OpenAlex.
- Artificial intelligence advances in cystoscopy and imaging for bladder cancer: a narrative review.The Canadian journal of urology · 2026Review
- A multi-modal approach for decision making in bladder cancer.Nature reviews. Urology · 2026Review
- Artificial intelligence and predictive tools in non-muscle invasive bladder cancer: a narrative review of current insights and advances.Translational andrology and urology · 2026Review
- A Novel Radiomics-based Interpretable Model for Bladder Cancer Grade Prediction Using White-Light Cystoscopy Images.European urology open science · 2026Article
- Artificial intelligence diagnostics for bladder tumor identification and grade prediction depend on narrow band imaging cystoscopy.iScience · 2026Article
- Explainable machine learning predicts overall survival in female bladder cancer patients after radical cystectomy.Discover oncology · 2025Article
- Integrating transcriptomics, single-cell omics, and deep learning-based histopathological features to identifyTranslational andrology and urology · 2025Article
- Beyond Visualization: Advanced Imaging, Theragnostics and Biomarker Integration in Urothelial Bladder Cancer.Cancers · 2025Review
- How Accurately Can Urologists Predict Eligible Patients for Immediate Postoperative Intravesical Chemotherapy in Bladder Cancer?Diagnostics (Basel, Switzerland) · 2025Article
- Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification.Discover oncology · 2025Review
- Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.Journal of imaging informatics in medicine · 2025Article
- Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence.Nature reviews. Urology · 2025Review
- Article
- Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images.Cancers · 2024Article
- New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities.Sensors (Basel, Switzerland) · 2024Review
- Evaluation of the Diagnostic Efficacy of the AI-Based Software INF-M01 in Detecting Suspicious Areas of Bladder Cancer Using Cystoscopy Images.Journal of clinical medicine · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Bladder Cancer and Artificial Intelligence: Emerging Applications.The Urologic clinics of North America · 2024Review
- Emerging Trends in Artificial Intelligence-Based Urological Imaging Technologies and Practical Applications.International neurourology journal · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluate the diagnostic performance of deep learning artificial intelligence (AI) for bladder cancer, which used white-light images (WLIs) and narrow-band images, and tumor grade prediction of AI based on tumor color using the red/green/blue (RGB) method. This retrospective study analyzed 10,991 cystoscopic images of suspicious bladder tumors using a mask region-based convolutional neural network with a ResNeXt-101-32 × 8d-FPN backbone. The diagnostic performance of AI was evaluated by calculating sensitivity, specificity, and diagnostic accuracy, and its ability to detect cancers was investigated using the dice score coefficient (DSC). Using the support vector machine model, we analyzed differences in tumor colors according to tumor grade using the RGB method. The sensitivity, specificity, diagnostic accuracy and DSC of AI were 95.0%, 93.7%, 94.1% and 74.7%. In WLIs, there were differences in red and blue values according to tumor grade (p < 0.001). According to the average RGB value, the performance was ≥ 98% for the diagnosis of benign vs. low-and high-grade tumors using WLIs and > 90% for the diagnosis of chronic non-specific inflammation vs. carcinoma in situ using WLIs. The diagnostic performance of the AI-assisted diagnosis was of high quality, and the AI could distinguish the tumor grade based on tumor color.
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