Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 11:36 PM
Ignite Modification Date:
2025-12-24 @ 11:36 PM
NCT ID:
NCT05193656
Brief Summary:
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.
Detailed Description:
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.
Study:
NCT05193656
Study Brief:
Protocol Section:
NCT05193656