Artificial Intelligence Detection of Bladder Tumors Under Endoscopy
Conditions: Bladder Tumor
Sponsor: Peking Union Medical College Hospital

Artificial Intelligence Detection of Bladder Tumors under Endoscopy  
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Artificial Intelligence Detection of Bladder Tumors under Endoscopy 
 
 
 
 
 
Study Protocol 
 
 
 
 
Version：3.0 
Date：2023-11-15

Artificial Intelligence Detection of Bladder Tumors under Endoscopy  
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1. Research Background 
Bladder cancer is the ninth most common malignancy worldwide, with an 
estimated 430,000 new cases diagnosed annually [1]. The standard diagnosis and 
monitoring of bladder cancer rely on white light cystoscopy (WLC), with over 2 
million cystoscopies performed annually in the United States and Europe [2]. Due to 
the high recurrence rate of bladder cancer, frequent monitoring and intervention are 
necessary. 
Early detection and complete resection of non-muscle invasive bladder cancer 
can reduce recurrence and progression. However, up to 40% of patients with 
multifocal disease do not achieve complete resection during the initial transurethral 
resection of bladder tumor (TURBT) [3]. Many papillary tumors and flat lesions are 
difficult to identify through WLC [4]. There is an urgent need for cost-effective, non-
invasive, and user-friendly adjunct imaging technologies to address the diagnostic 
deficiencies of WLC. 
Recent advancements in deep learning-based automated image processing may 
provide new solutions to the limitations of cystoscopy [5]. Convolutional neural 
networks (CNNs) possess the ability to learn complex relationships and integrate 
existing knowledge into models, showing potential applications across various fields, 
including bladder tumor diagnosis [6]. We employed the HRNet algorithm, a 
convolutional neural network, for enhanced bladder tumor detection. 
 
2. Research Objectives 
We aim to explore the potential application of AI in urological tumors by 
collecting cystoscopy videos from patients undergoing cystoscopy. These videos 
including bladder tumors will be annotated manually by urologists, these annotated 
videos will then be used to train the AI algorithm to recognize the bladder tumors. We 
will compare the effect of AI algorithm with urologists. 
 
3. Research Methods 
This is a multicenter, retrospective, observational study. 
 
4. Research Process 
4.1 Patient enrollment 
Inclusion criteria: 1. The patients who had bladder tumor and received WLC or 
TURBT, and the full-length surgery video is available. 
Exclusion criteria:  
1. The video is too blurry to distinguish the normal bladder wall and bladder 
tumor. 
2. Lack of the appearance of bladder tumor before resection. 
3. Lack of informed consent.  
Patient personal information in the videos will not be shown.  
Videos from the initially recruited 200 bladder tumor patients will be used for 
algorithm training and validation. Then, we compare the urologist and algorithm 
capability of detecting bladder tumor. 
 
4.2 Data Preprocessing 
To reduce the data volume, we extract the frame at a ratio of 1:4. Two urologists 
outline the boundary of bladder tumors in each frame separately and check for each 
other. AI algorithm is used to contour the same bladder tumors.  The outlines of 

Artificial Intelligence Detection of Bladder Tumors under Endoscopy  
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bladder tumor annotated by urologists and algorithm are compared, the IOU value, 
precision, sensitivity and false negative rate are analyzed. 
 
4.3 Algorithm Development 
This study uses semantic segmentation to identify bladder tumors in WLC. The 
D-LinkNet network structure uses a pre-trained ResNet34 on the ImageNet dataset as 
its encoder, with the central part utilizing dilated convolutions with different dilation 
rates in a cascaded manner, and upsampling performed using deconvolution [7]. The 
original resolution of all images is 1920×1080, downsampled by 2 to 960×540, and 
zero-padded in the width direction to obtain the image with a resolution of 960×544. 
The RGB images of this size are normalized, mean-subtracted, and variance-divided 
before being input into the network. The images undergo five encoding processes, 
dilated convolutions, and five decoding processes, ultimately producing a prediction 
result of 960×544, which is further post-processed. In this research, the parameter 
settings are as follows: batch size of 8, Adam optimizer, a learning rate of 0.001. The 
training environment is an NVIDIA TITAN Xp GPU. 
 
4.4 Results Interpretation 
The intersection over union (IOU) is a crucial standard for evaluating single-
frame image recognition capability in image recognition [8]. When IOU is above the 
threshold, it suggests that the algorithm detects the object successfully, indicating a 
true positive. When IOU cannot reach the threshold, it suggests that the algorithm 
fails to detect the object, and indicating a false negative. If a prediction appears 
without ground truth in the image, it is considered a false positive. We calculate the 
algorithm's sensitivity and precision in the test set. The Dice coefficient measures the 
similarity between two samples. A higher average Dice coefficient indicates a better 
detection performance of the algorithm. 
 
4.5 Indicators 
① We classify the videos in test and validation groups according to the 
discernibility of bladder tumors in the video;  
② We observe the algorithm's sensitivity, precision, false negative rate, false 
positive rate, and average Dice coefficient at IOU thresholds of 0.1 and 0.5 in the test 
set under different discernibility groups. 
 
4.6 Statistical Methods 
Analysis will be analyzed by R 4.0.2 software; the data will be expressed as 
absolute numbers or percentages. 
 
5. Data Management and Confidentiality 
All data in this study is properly stored to ensure security without loss or leakage. 
Sensitive information and patient information will not be uploaded to public platforms. 
During data processing, patients' personal information will be anonymized, and 
patient identification codes will be used to replace patient names and IDs. If technical 
services are needed, data will be appropriately encrypted, and a confidentiality 
agreement will be signed. 

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