Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study

Chang, T. N.; Shkolyar, Eugene; Giudice, Francesco Del; Eminaga, Okyaz; Lee, Timothy J.; Laurie, Mark; Seufert, Caleb; Xiao, Jia; Mach, Kathleen E.; Xing, Lei; Liao, Joseph C.

doi:10.1089/end.2023.0056

Cited by 9 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moving forward, the incorporation of more data types (including CT or MR imaging data and special imaging cystoscopy) and optimization of the model structure can lead to enhanced accuracy in tumor recognition [27,28], enabling the classification and refinement of different types and stages of bladder tumors. Chang TC, Nairveen Ali, Barrios W et al [27,[29][30][31] have attempted to utilize different types of CNNs for the classification of bladder tumor histology or invasiveness. According to their findings, different algorithms have demonstrated their own advantages in specific recognition tasks.…”

Section: Discussionmentioning

confidence: 99%

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis

Guo,

Li,

Zhang

et al. 2023

Urol Int

View full text Add to dashboard Cite

Introduction: Bladder cancer (BC) is a major health concern that poses a significant threat to the population, with an increasing incidence rate and a high risk of recurrence and progression. The primary clinical method for diagnosing bladder cancer is cystoscopy, but due to the limitations of traditional white light cystoscopy and inadequate clinical experience among junior physicians, its detection rate for bladder tumor, especially small and flat lesions, is relatively low. However, recent years have seen remarkable advancements in the application of artificial intelligence (AI) technology in the field of medicine. This has led to the development of numerous AI algorithms that have been successfully integrated into medical practices, providing valuable assistance to clinicians. The purpose of this study is to develop a cystoscopy algorithm that is real-time, cost-effective, high-performing, and accurate, with the aim of enhancing the detection rate of bladder tumors during cystoscopy. Materials and methods: For this study, a dataset of 3500 cystoscopic images obtained from 100 patients diagnosed with bladder cancer was collected, and a deep learning model was developed utilizing the U-Net algorithm within a convolutional neural network for training purposes. Results: This study randomly divided 3,500 images from 100 bladder cancer patients into training and validation groups, and each patient’s pathology result was confirmed. In the validation group, the accuracy of tumor recognition by the U-Net algorithm reached 98%. Compared to primary urologists, with greater accuracy and faster detection speed. Conclusion: This study highlights the potential of U-Net-based deep learning techniques in the detection of bladder tumors. The establishment and optimization of the U-Net model is a significant breakthrough and it provides a valuable reference for future research in the field of medical image processing.

show abstract

Section: Discussionmentioning

confidence: 99%

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis

Guo,

Li,

Zhang

et al. 2023

Urol Int

View full text Add to dashboard Cite

show abstract

“…Qualitatively, the NeRF reconstruction provided increased contrast to color and topography compared to SfM, while also ensuring more complete coverage. These are the most important qualities of a digital record of a remote bladder examination by a remote urologist or using AI computer-aided diagnostic tools, 40 which is the motivation for this work. Although the spatial resolution was not measured, the NeRF reconstruction did not show any noticeable reduction in resolution or distortion, while the SfM reconstruction was susceptible to dropouts and artifacts.…”

Section: Comparisonsmentioning

confidence: 99%

Enabling rapid and high-quality 3D scene reconstruction in cystoscopy through neural radiance fields

Chen,

Gunderson,

Lewis

et al. 2024

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling

View full text Add to dashboard Cite

3D reconstruction of cystoscopy holds substantial value in the observation and guided treatment of urological conditions. 3D models of the bladder, obtained through the reconstruction, can aid physicians in performing a rapid and comprehensive assessment of various conditions, such as detection of bladder cancer and followup surveillance throughout the patients' life. In recent years, significant advancements have been made in 3D reconstruction based on cystoscopy. However, the broader application of these advancements has been hampered by issues such as texture loss, slow computational speed, and susceptibility to interference. In this study, we have achieved, for the first time, a reconstruction of a dynamic cystoscopy scene using Neural Radiance Fields (NeRF). Unlike mesh-based 3D reconstruction, NeRF can restore scenes under conditions where the number of views and features are limited, thereby addressing the problem of texture loss that might arise from traditional 3D reconstruction algorithms. Additionally, to expedite the computation speed of NeRF, we employed Instant-NGP, an open-source software that accelerates NeRF computations using hash encoding. This potentially reduces computation time by a hundredfold, making it several tens of times faster than SfM when camera pose values can be streamed into the NeRF reconstruction. By comparing NeRF and SfM methods, we found that NeRF exhibits stronger resistance to interference than SfM, underscoring the formidable potential of NeRF as an innovative method in the field of endoscopy. In the future of robotic-assisted flexible cystoscopy, NeRF has the potential to provide rapid, robust, and comprehensive recording for remote diagnosis of bladder abnormalities.

show abstract

Bladder cancer diagnosis with AI, cystoscopy and pathomics

Laurie,

Ge,

Shkolyar

et al. 2025

Artificial Intelligence in Urology

View full text Add to dashboard Cite

Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study

Cited by 9 publications

References 12 publications

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis

U-Net-Based Assistive Identification of Bladder Cancer: A Promising Approach for Improved Diagnosis

Enabling rapid and high-quality 3D scene reconstruction in cystoscopy through neural radiance fields

Bladder cancer diagnosis with AI, cystoscopy and pathomics

Contact Info

Product

Resources

About