An artificial intelligence system for the diagnosis and risk stratification of chronic atrophic gastritis under white light endoscopy
Revolutionizing gastric cancer diagnostics through advanced data analysis and expert collaboration.
Innovative AI for Endoscopy Insights
At Gastrolume AI, we enhance endoscopic diagnosis through advanced data analysis and expert collaboration, ensuring accurate staging and improved patient outcomes in gastric health.
Advanced Endoscopy Solutions
Innovative AI-driven diagnostic services for enhanced endoscopy and pathology analysis.
Data Annotation Experts
We meticulously annotate endoscopy reports and pathology descriptions for precise staging and grading.
AI Retrieval Systems
Utilizing advanced vector retrieval to enhance diagnosis through similar-case analysis and guideline integration.
Endoscopy Insights
Comprehensive analysis through structured data and expert annotations.
Phase One
Data collection and expert annotation of endoscopy reports.
Phase Two
Prompt engineering and retrieval for diagnostic suggestions enhancement.
Chronic atrophic gastritis (CAG) is a key precancerous lesion for gastric cancer; its early detection and precise risk stratification are critical for improving patient survival and reducing healthcare costs. Traditional diagnosis depends on endoscopists’ experience and pathology, which are subjective, time-consuming, and limited in accessibility. Therefore, our central research question is: Can a fine-tuned GPT-4 model deeply integrate white-light endoscopy image reports, pathological descriptions, and clinical indicators (e.g., H. pylori status, gastric acidity) to automatically generate medically interpretable CAG diagnostic conclusions and risk stratification recommendations?
Sub-questions include:
Multimodal Feature Extraction: How to engineer prompts that extract key features from endoscopy descriptions, pathology reports, and clinical data?
Staging Determination: Can the model accurately classify CAG into mild, moderate, and severe stages based on distribution of atrophy and intestinal metaplasia, aligned with OLGA/OLGIM staging systems?
Cancer-Risk Prediction: Integrating age, family history, and pathology dysplasia grades, can GPT-4 output five-year probabilities of progression from CAG to gastric cancer?
Explainability & Consistency: Does the model’s output include clear causal reasoning chains that both endoscopists and pathologists can review and reach high agreement?
We hypothesize that combining retrieval-augmented generation with GPT-4 fine-tuning will achieve ≥88% accuracy in staging, a risk-prediction AUC ≥0.85, and explanation coherence agreement ≥0.9 among clinicians.