Ignite Creation Date:
2024-05-19 @ 5:34 PM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06412419
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-05-14
First Post:
2024-05-06
Brief Title:
Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma
Sponsor:
Changhai Hospital
Organization:
Changhai Hospital
Study Overview
Official Title:
Based on Multimodal Endoscopy and Weakly Supervised Deep Learning-Early Esophageal Squamous Cell Carcinoma Infiltration Depth Precise Prediction Study
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The objective of this project is to pioneer a novel protocol for the adjunctive screening of early-stage esophageal cancer and its precancerous lesions The anticipated outcomes include simplifying the training process for users shortening the duration of examinations and achieving a more precise assessment of the extent of esophageal cancer invasion than what is currently possible with ultrasound technology This research endeavors to harness the synergy of endoscopic ultrasound EUS and Magnifying endoscopy augmented by the pattern recognition and correlation capabilities of artificial intelligence AI to detect early esophageal squamous cell carcinoma and its invasiveness along with high-grade intraepithelial neoplasia The overarching goal is to ascertain the potential and significance of this approach in the early detection of esophageal cancer
The projects primary goals are to develop three distinct AI-assisted diagnostic systems
An AI-driven electronic endoscopic diagnosis system designed to autonomously identify lesions
An AI-based EUS diagnostic system capable of automatically delineating the affected areas
A multimodal diagnostic framework that integrates electronic endoscopy with EUS to enhance diagnostic accuracy and efficiency
The projects primary goals are to develop three distinct AI-assisted diagnostic systems
An AI-driven electronic endoscopic diagnosis system designed to autonomously identify lesions
An AI-based EUS diagnostic system capable of automatically delineating the affected areas
A multimodal diagnostic framework that integrates electronic endoscopy with EUS to enhance diagnostic accuracy and efficiency
Detailed Description:
The study was executed in two distinct phases The initial phase designated as the modeling phase Phase 1 involved a retrospective analysis of eligible subjects from a consortium of medical institutions including the First Affiliated Hospital of Naval Medical University West China Hospital of Sichuan University Provincial Hospital Affiliated to Shandong First Medical University the First Affiliated Hospital of Soochow University the First Affiliated Hospital of Henan University of Science and Technology and the First Affiliated Hospital of Shihezi University all selected prior to January 1 2024 The second phase known as the real-world evaluation phase Phase 2 prospectively enrolled consecutive patients who were scheduled to undergo magnometric endoscopy and EUS at the aforementioned hospitals between April 2024 and June 2024
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None