Clinical trial NCT05459610: Automatic Evaluation of the Extent of Intestinal Metaplasia With Artificial Intelligence
Official title: Development and Validation of an Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Intestinal Metaplasia of Gastric Mucosa
Artificial Intelligence
Endoscopy
Lead sponsor: Shandong University
Sponsor class: OTHER
First posted: 2022-07-15
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Consecutive patients who receive the IEE examination and screened that fulfill the eligibility criteria at Qilu Hospital, Shandong University will be enrolled into the study

Brief summary: Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, the high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.

Detailed description: Globally, gastric cancer is the fifth most prevalent malignancy and the third leading cause of cancer mortality. Gastric intestinal metaplasia (GIM) is an intermediate precancerous gastric lesion in the gastric cancer cascade. Studies have shown that the 5-year cumulative incidence of gastric cancer in IM patients ranges from 5.3% to 9.8% . With technical advance of image-enhanced endoscopy (IEE), studies have demonstrated IEE has high accuracy for diagnosis of GIM. The endoscopic grading system (EGGIM), a new endoscopic risk scoring system for GC, have been shown to accurately identify a wide range of patients with GIM. However, The high diagnostic accuracy of GIM using IEE and EGGIM assessments performed all require much experience, which limits the application of EGGIM. The investigators aim to design a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores.

Eligibility criteria: Inclusion Criteria:

* patients aged 18-80 years who undergo the IEE examination

Exclusion Criteria:

* patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in gastroscopy
* patients with previous surgical procedures on the stomach
* patients who refuse to sign the informed consent form

Primary outcomes:
measure: The specificity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
timeFrame: 2 years
measure: The accuracy of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
timeFrame: 2 years
measure: The sensitivity of AI model to assess the degree of intestinal metaplasia in an endoscopic picture
timeFrame: 2 years
Secondary outcomes:
measure: Accuracy of the experienced endoscopists to assess the degree of intestinal metaplasia
timeFrame: 2 years
measure: Accuracy of the inexperienced endoscopists to assess the degree of intestinal metaplasia
timeFrame: 2 years
measure: Inter-observer agreement among experienced endoscopists in identifying the degree of intestinal metaplasia
timeFrame: 2 years
measure: Inter-observer agreement among inexperienced endoscopists in identifying degree of intestinal metaplasia
timeFrame: 2 years
Other outcomes:
Locations:
city: Jinan
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: