MedTrace Logo

Multimodal Endoscopic Image Fusion for Assessing Infiltration in Superficial Esophageal Squamous Cell Carcinoma

NCT06412419 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 450

Last updated 2024-05-14

No results posted yet for this study

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 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.

Conditions

  • Esophageal Neoplasms Malignant

Interventions

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

  • West China Hospital

    collaborator OTHER

  • Shandong Provincial Hospital

    collaborator OTHER_GOV

  • The First Affiliated Hospital of Soochow University

    collaborator OTHER

  • The First Affiliated Hospital of Henan University of Science and Technology

    collaborator OTHER

  • THE FIRST AFFILIATED HOSPITAL OF SHIHEZI UNIVERSITY

    collaborator UNKNOWN

  • Changhai Hospital

    lead OTHER

Principal Investigators

  • Luowei Wang · Changhai Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-15
Primary Completion
2024-08-30
Completion
2024-10-30

Countries

  • China

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06412419 on ClinicalTrials.gov