AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging

Condition(s) studied

Investigational drug(s) / intervention(s)

No intervention (observational study): This is an observational study with no intervention assigned to participants.

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.

Eligibility

Primary outcome measure(s)

• Diagnostic performance of the artificial intelligence model for detecting early gastric cancer — Up to 14 days after endoscopy, when histopathological results are available
  The primary outcome is the diagnostic performance of the artificial intelligence model for identifying early gastric cancer, evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score, using histopathological diagnosis as the reference standard.

Trial sites (1)

More The First Affiliated Hospital of Soochow University trials in China

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