Clinical trial NCT05464108: Validation the Artificial Intelligence System for Automatic Evaluation of the Extent of Intestinal Metaplasia
Official title: Validation the Artificial Intelligence System for Automatic Evaluation of the Extent of
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Intestinal Metaplasia of Gastric Mucosa
Artificial Intelligence
Endoscopy
Lead sponsor: Shandong University
Sponsor class: OTHER
First posted: 2022-07-19
Enrollment: 1080
Sex: ALL
Minimum age (years): 40
Maximum age (years): 75
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 will be enrolled into the study

Brief summary: The operative link on gastric intestinal metaplasia assessment (OLGIM) staging systems using biopsy specimens were commonly used for histological assessment of gastric cancer risk. But its clinical application is limited for at least biopsy samples. The endoscopic grading system (EGGIM) has been shown a significant correlation with the OLGIM. The investigators designed a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores in endoscopy examination. This study is aimed at exploring the relevance of the EGGIM scores automatically evaluated by Artificial Intelligence and OLGIM scores.

Detailed description: Gastric intestinal metaplasia(GIM) is an important stage in the gastric cancer(GC). The operative link on gastric intestinal metaplasia assessment (OLGIM) staging systems using biopsy specimens were commonly used for histological assessment of gastric cancer risk. However, its need to take at least 4 biopsies is not clinically feasible. The endoscopic grading system (EGGIM) has been shown a significant correlation with the OLGIM. An EGGIM score of 5 was the best cut off value for identifying OLGIM stage III/IV patients. The investigators have designed a computer-aided diagnosis program using deep neural network to automatically evaluate the extent of IM and calculate the EGGIM scores in endoscopy examination. This study is aimed at exploring the relevance of the EGGIM scores automatically evaluated by Artificial Intelligence and OLGIM scores.

Eligibility criteria: Inclusion Criteria:

* patients aged 40-75 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 with contraindications to biopsy
* patients who refuse to sign the informed consent form

Primary outcomes:
measure: Accuracy of AI model to diagnose extensive intestinal metaplasia (OLGIM stage III/IV) by calculating the EGGIM score
timeFrame: 2 years
Secondary outcomes:
measure: Accuracy of experienced endoscopists to diagnose extensive intestinal metaplasia (OLGIM stage III/IV) by calculating the EGGIM score
timeFrame: 2 years
measure: Accuracy of inexperienced endoscopists to diagnose extensive intestinal metaplasia (OLGIM stage III/IV) by calculating the EGGIM score
timeFrame: 2 years
measure: Inter-observer agreement among experienced endoscopists in identifying the EGGIM scores
timeFrame: 2 years
measure: Inter-observer agreement among inexperienced endoscopists in identifying the EGGIM scores
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: