Actively Recruiting
Development and Validation of an Explainable Artificial Intelligence Model for Early Gastric Cancer Diagnosis Using Multimodal Endoscopic Imaging
Led by The First Affiliated Hospital of Soochow University · Updated on 2026-04-24
100
Participants Needed
1
Research Sites
8 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Researchers are investigating an explainable multimodal artificial intelligence (AI) model to improve the diagnosis of early gastric cancer (EGC) using endoscopic imaging. The study addresses challenges in accurately detecting EGC due to subtle features and variability among endoscopists. The goal is to develop a reliable AI tool that combines multiple imaging types and clinical data, enhancing diagnostic accuracy and interpretability during endoscopic examinations. The study collects data from patients who underwent endoscopic submucosal dissection (ESD) with confirmed early gastric cancer or non-cancerous lesions. It uses white-light imaging (WLI) and image-enhanced endoscopy to gather representative images. The process includes developing a lesion detection model, extracting quantitative image features, and using deep learning to analyze images. These features are integrated with clinical information to build a multimodal AI prediction model. Participants provide endoscopic images and clinical data, which are divided into training, validation, and testing groups for model evaluation. The AI model's diagnostic performance is measured using metrics like the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. The study also applies methods to explain and visualize the AI's decision-making process, aiming to support clinical use of AI-assisted diagnosis. Evaluation occurs up to 14 days after endoscopy when histopathological results are available.
CONDITIONS
Brief Title
AI Model for Early Gastric Cancer Diagnosis Using Endoscopic Imaging
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Age 18 years
- Suspicious gastric lesions identified on white-light imaging (WLI)
- Preoperative biopsy indicating precancerous lesions (dysplasia or intraepithelial neoplasia) or adenocarcinoma
- Preoperative magnifying endoscopy with narrow-band imaging (ME-NBI) performed
- Patients who meet absolute indications for endoscopic submucosal dissection (ESD) and have undergone ESD
You will not qualify if you...
- Non-adenocarcinoma histological types (e.g., lymphoma)
- Patients who did not undergo ME-NBI examination or did not receive ESD
- Lesions invading the muscularis propria or deeper layers
- Missing or indeterminate postoperative histopathological results
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Your Study Journey
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - Up to 14 days after endoscopy
Participants undergo endoscopic examination including white-light imaging and image-enhanced endoscopy to identify and assess gastric lesions.
1 visit (in-person)
Duration - Up to several months as data is collected and analyzed
Participants who have undergone endoscopic submucosal dissection are observed as their imaging and clinical data are collected for AI model development and validation.
Trial Site Locations
Total: 1 location
1
The First Affiliated Hospital of Soochow University
Suzhou, China
Actively Recruiting
Research Team
L
Li he Liu
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
2
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