Study on the Effectiveness of Gastroscope Operation Quality Control Based on Artificial Intelligence Technology

This study aims to construct a real-time quality monitoring system based on artificial intelligence technology.

Study Overview

• Status: Completed
• Conditions: Gastric Cancer
• Intervention / Treatment: Diagnostic test: blind spots

Detailed Description

Gastroscopy plays an important role in the detection and diagnosis of upper gastrointestinal diseases. It is necessary for endoscopists to operate gastroscope according to the standardized process, in order to avoid missing early lesions. However, with the rapid increase in the number of endoscopies, the workload of endoscopists increases further. High workload reduces the quality of endoscopy, resulting in incomplete observation of anatomical parts that are easy to be missed in the process of gastroscopy. There are significant differences in the operation level of different endoscopists. Therefore, carrying out artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment.

Artificial intelligence devices need to use a large number of endoscopic images, based on this, we intends to collect endoscopic image data from our hospitals for training and validation of the model.

• Study Type: Observational
• Enrollment (Actual): 1570

Contacts and Locations

Study Locations

• China
  • Haidian
    • Beijing, Haidian, China, 100142
      • Beijing Cancer Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 75 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Patients who meet the criteria for gastroscopy examination.

Description

Inclusion Criteria:

1. Patiens aged 18 years or above undergoing gastroscopy；
2. Be able to read, understand and sign informed consent；

Exclusion Criteria:

1. Patients with absolute contraindications to endoscopy examination;
2. pregnant women;
3. previous history of gastric surgery;
4. the researcher considers that the subject is not suitable for clinical trial.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy	Calculate the accuracy of AI's judgment on images	2020.2.22-2020.7.1
Sensitivity	number of images in which AI correctly diagnosed positive/all images with positive	2020.2.22-2020.7.1
Specificity	number of images in which AI correctly diagnosed negative/all images negative	2020.2.22-2020.7.1

Collaborators and Investigators

• Sponsor: Peking University
• Investigators: Study Chair: Qi Wu, MD., Peking University Cancer Hospital & Institute

Study record dates

Study Major Dates

• Study Start (Actual): February 22, 2020
• Primary Completion (Actual): January 13, 2022
• Study Completion (Actual): May 1, 2022

Study Registration Dates

• First Submitted: May 8, 2020
• First Submitted That Met QC Criteria: May 8, 2020
• First Posted (Actual): May 12, 2020

Study Record Updates

• Last Update Posted (Actual): November 29, 2023
• Last Update Submitted That Met QC Criteria: November 28, 2023
• Last Verified: November 1, 2023

More Information

Terms related to this study

• Keywords: artificial intelligence; gastric cancer; endoscopy
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Stomach Diseases; Stomach Neoplasms
• Other Study ID Numbers: PX2020047

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No