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An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment

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Published on: July 28, 2012

Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.

Jeong Woo Yoo¹, Kyo Chul Koo¹, Byung Ha Chung¹

¹Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, 211 Eonju-Ro, Gangnam-Gu, Seoul, 06273, Republic of Korea.

Scientific Reports

|October 22, 2022

Summary

Deep learning artificial intelligence (AI) demonstrates high diagnostic performance for bladder cancer detection and grading using cystoscopic images. The AI system accurately differentiates tumor grades based on color analysis, improving diagnostic capabilities.

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A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

Area of Science:

Urology
Oncology
Medical Imaging
Artificial Intelligence

Background:

Bladder cancer diagnosis relies on cystoscopy, but accuracy can be limited.
Artificial intelligence (AI) offers potential for enhanced diagnostic performance in detecting and grading bladder tumors.
Objective assessment of tumor characteristics, such as color, may aid in diagnosis.

Purpose of the Study:

To evaluate the diagnostic performance of AI using white-light images (WLIs) and narrow-band imaging for bladder cancer detection.
To assess AI's capability in predicting bladder tumor grade based on color analysis using the red/green/blue (RGB) method.
To investigate the accuracy of AI in differentiating various bladder tumor types and grades.

Main Methods:

A retrospective analysis of 10,991 cystoscopic images of suspicious bladder tumors was performed.
A deep learning model (mask region-based convolutional neural network with ResNeXt-101-32×8d-FPN backbone) was utilized for image analysis.
Sensitivity, specificity, diagnostic accuracy, and dice score coefficient (DSC) were calculated to evaluate AI performance.
A support vector machine model analyzed tumor color differences (RGB values) correlated with tumor grade.

Main Results:

The AI achieved high diagnostic performance with sensitivity (95.0%), specificity (93.7%), and diagnostic accuracy (94.1%).
The AI demonstrated a dice score coefficient (DSC) of 74.7% for cancer detection.
Significant differences in red and blue color values were observed between tumor grades (p < 0.001).
AI achieved ≥98% accuracy in diagnosing benign vs. low- and high-grade tumors and >90% for chronic non-specific inflammation vs. carcinoma in situ using WLIs.

Conclusions:

AI-assisted diagnosis for bladder cancer shows high quality and accuracy.
The AI system can effectively distinguish tumor grades by analyzing tumor color.
AI holds significant promise for improving the accuracy and efficiency of bladder cancer diagnosis and grading.