Clinical trial NCT05916014: AI-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis
Official title: Artificial Intelligence-assisted White Light Endoscopy to Identify the Kimura-Takemoto Classification of Atrophic Gastritis to Achieve Gastric Cancer Risk Assessment
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Atrophic Gastritis
Artificial Intelligence
Endoscopy
Lead sponsor: Shandong University
Sponsor class: OTHER
Collaborators: name: Linyi County People's Hospital,Dezhou,China
First posted: 2023-06-23
Enrollment: 1500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Consecutive patients who receive the gastrointestinal endoscopy examination and screened that fulfill the eligibility criteria at Qilu Hospital,Shandong University,Linyi County People's Hospital will be enrolled into the study

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.

Eligibility criteria: 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;

Primary outcomes:
measure: Accuracy of AI model to diagnose the Kimura-Takemoto classification
timeFrame: 2 years
measure: Sensitivity of AI model to diagnose the Kimura-Takemoto classification
timeFrame: 2 years
measure: Specificity of AI model to diagnose the Kimura-Takemoto classification
timeFrame: 2 years
Secondary outcomes:
measure: The MIOU value of AI model in semantic segmentation of endoscopic atrophy picture
timeFrame: 2 years
Other outcomes:
Locations:
city: Shangdong
country: China
facility: Department of Gastrology, QiLu Hospital, Shandong University
state: Shandong
Study officials:
affiliation: Qilu Hospital, Shandong University
name: yanqing li, MD,PHD
role: STUDY_CHAIR
References: