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AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging

NCT07551466 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 100

Last updated 2026-04-24

No results posted yet for this study

Summary

Early gastric cancer (EGC) is often difficult to detect accurately during endoscopic examination due to subtle morphological features and variability among endoscopists. Artificial intelligence (AI) has shown promise in improving diagnostic performance; however, most existing models lack interpretability and rely on single-modality imaging.

This study aims to develop and evaluate an explainable multimodal artificial intelligence model for the diagnosis of early gastric cancer using endoscopic imaging. The model integrates features derived from white-light imaging and image-enhanced endoscopy, along with quantitative image features and clinical data, to improve diagnostic accuracy and provide interpretable decision support.

The primary outcome is the diagnostic performance of the AI model for detecting early gastric cancer, evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

The results of this study are expected to provide evidence for the clinical utility of explainable AI in endoscopic diagnosis and support the development of reliable human-AI collaborative diagnostic systems.

Conditions

  • Early Gastric Cancer

Interventions

OTHER

No intervention (observational study)

This is an observational study with no intervention assigned to participants.

Sponsors & Collaborators

  • The First Affiliated Hospital of Soochow University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-05-01
Primary Completion
2026-12-01
Completion
2027-02-01

Countries

  • China

Study Locations

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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 NCT07551466 on ClinicalTrials.gov