Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma
NCT ID: NCT06412419
Last Updated: 2024-05-14
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
450 participants
OBSERVATIONAL
2024-05-15
2024-10-30
Brief Summary
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The project's 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.
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Detailed Description
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Conditions
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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Low-grade intraepithelial neoplasia of esophageal squamous epithelium
Magnifying Endoscopy and Endoscopic Ultrasonography
The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.
High-grade intraepithelial neoplasia of esophageal squamous epithelium
Magnifying Endoscopy and Endoscopic Ultrasonography
The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.
Stage T1a esophageal squamous cell carcinoma
Magnifying Endoscopy and Endoscopic Ultrasonography
The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.
Stage T1b esophageal squamous cell carcinoma
Magnifying Endoscopy and Endoscopic Ultrasonography
The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.
Interventions
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Magnifying Endoscopy and Endoscopic Ultrasonography
The acquired magnifying endoscopy and endoscopic ultrasonography images were shared with artificial intelligence for machine learning, diagnostic modeling and optimization. In the real world evaluation phase, the high-risk population of early esophageal cancer who planned to undergo esophageal electronic endoscopy were prospectively enrolled. The artificial intelligence-assisted diagnosis system was used for prediction before surgery, and the postoperative pathological results were used as the gold standard to diagnose by grouping.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
Patients with significant endoscopic, imaging, or pathological evidence of advanced esophageal cancer.
Patients presenting with marked esophageal stenosis or dilatation. Individuals with a history of other malignancies. Patients who have received neoadjuvant radiotherapy. Patients who declined to participate in the study and did not provide informed consent.
18 Years
ALL
No
Sponsors
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West China Hospital
OTHER
Shandong Provincial Hospital
OTHER_GOV
The First Affiliated Hospital of Soochow University
OTHER
The First Affiliated Hospital of Henan University of Science and Technology
OTHER
THE FIRST AFFILIATED HOSPITAL OF SHIHEZI UNIVERSITY
UNKNOWN
Changhai Hospital
OTHER
Responsible Party
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Principal Investigators
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Luowei Wang
Role: STUDY_CHAIR
Changhai Hospital
Locations
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Changhai hospital
Shanghai, , China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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MEIFI-sESCC
Identifier Type: -
Identifier Source: org_study_id
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