Artificial Intelligence, Atrophic Gastritis, Endoscopy
Conditions
Brief summary
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification.
Detailed description
Grading endoscopic atrophy according to the Kimura-Takemoto classification can assess the risk of gastric neoplasia development. The higher the score, the more severe the degree of atrophic gastritis. However, the false negative rate of chronic atrophic gastritis is high due to the varying diagnostic standardization and diagnostic experience and levels of endoscopists. Therefore, this study aims to develop an AI model to identify the Kimura-Takemoto classification of atrophic gastritis to achieve gastric cancer risk assessment.
Interventions
DIAGNOSTIC_TESTDiagnostic Test: The diagnosis of Artificial Intelligence and endosopists
Endosopists and AI will assess the Kimura-Takemoto classification independently when the patients is eligible.
Sponsors
Linyi County People's Hospital,Dezhou,China
Shandong University
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
Patients aged 18-80 years who undergo the white light endoscope examination Informed consent form provided by the patient.
Exclusion criteria
1. patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric; 2. disorders who cannot participate in gastroscopy; 3. Patients with progressive gastric cancer; 4. low quality pictures; 5. patients with previous surgical procedures on the stomach or esophageal; 6. patients who refuse to sign the informed consent form;
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of AI model to diagnose the Kimura-Takemoto classification | 2 years | Accuracy of AI model to diagnose the Kimura-Takemoto classification |
| Sensitivity of AI model to diagnose the Kimura-Takemoto classification | 2 years | Sensitivity of AI model to diagnose the Kimura-Takemoto classification |
| Specificity of AI model to diagnose the Kimura-Takemoto classification | 2 years | Specificity of AI model to diagnose the Kimura-Takemoto classification |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture | 2 years | The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture |
Countries
China
Contacts
Primary Contactyanqing Li, MD, PHD
Outcome results
None listed