Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

Not Applicable

Completed

• Conditions: Gastric Cancer
• Interventions: Diagnostic Test: AI-based diagnosis; Diagnostic Test: The expert endoscopists-based diagnosis

• Registration Number: NCT04040374
• Lead Sponsor: Tokyo University

Brief Summary

Title: A single-center, retrospective randomized controlled trial of artificial intelligence (AI) versus expert endoscopists for diagnosis of gastric cancer in patients who underwent upper gastrointestinal endoscopy.

Précis: this single-center, retrospective randomized controlled trial will include 500 outpatients who underwent upper gastrointestinal endoscopy for gastric cancer screening and will compare the diagnostic detection rate for gastric cancer of AI and expert endoscopists.

Objectives Primary Objective: to evaluate the diagnostic detection rate for gastric cancer of AI and expert endoscopists.

Secondary Objectives: to determine whether AI is not inferior to expert endoscopists in terms of the number of images analyzed for diagnosis of gastric cancer and intersection over union (IOU), and the detection rate of diagnosis of early and advanced gastric cancer.

Endpoints Primary Endpoint: diagnosis of gastric cancer. Secondary Endpoints: image based diagnosis of gastric cancer and IOU. Population: in total, 500 males and females aged ≥ 20 years who underwent upper gastrointestinal endoscopy for screening of gastric cancer at a single hospital in Japan.

Describe the Intervention: AI-based diagnosis of gastric cancer based on upper gastrointestinal endoscopy images.

Study Duration: 3 months.

Detailed Description

Prior to Study: Total 500: Screen potential subjects by inclusion and exclusion criteria; obtain endoscopy images.

Randomization was performed.

Intervention: AI diagnosis was performed for 250 patients using upper gastrointestinal endoscopy images, and Expert endoscopists diagnosis was performed for 250 patients by same methods.

Primary analysis: Perform primary analysis of primary and secondary endpoints for 250 patients in each group

Cross over diagnosis between AI and expert endoscopists was performed.

Perform secondary analysis of agreement of gastric cancer diagnosis per images and IOU between AI and expert endoscopists for 500 patients.

Study Timeline

• Study Start: 2019-07-01
• Study First Submitted: 2019-07-19
• Study First Submitted QC: 2019-07-29
• Study First Posted: 2019-07-31
• Primary Completion: 2019-10-01
• Status Verified: 2019-11
• Study Completion: 2019-11-16
• Last Update Submitted: 2019-11-19
• Last Update Posted: 2019-11-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 500

Inclusion Criteria

1. Males or females aged ≥ 20 years who underwent upper gastrointestinal endoscopy at Tokyo University Hospital during 2018.
2. Informed optout consent, obtained from each patient before completion of the study.

Exclusion Criteria

1. Patients who underwent gastrectomy.
2. Patients who underwent transnasal upper gastrointestinal endoscopy.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
AI-based diagnosis	AI-based diagnosis	• AI-based diagnosis will be performed based on analysis of endoscopic images (Olympus Optical, Tokyo, Japan). The investigators will use the Single Shot MultiBox Detector (SSD), a deep neural network architecture (https://arxiv.org/abs/1512.02325), and an optimal diagnostic cutoff from a prior report2. The AI system reviewed endoscopy images and reported those in which gastric cancer was detected, together with the coordinates (X, Y) of the lesions.
Expert endoscopist diagnosis	The expert endoscopists-based diagnosis	The expert endoscopists are two physicians with experience of more than 20,000 endoscopies. The expert endoscopists will review the endoscopy images of each patient for 5 min. They will then report endoscopy images in which gastric cancer was detected and manually annotate the lesions in those images.

Primary Outcome Measures

Name	Time	Method
Per patient diagnosis of gastric cancer	Up to 6 weeks from study start	Number of Participants

Secondary Outcome Measures

Name	Time	Method
Diagnosis of advanced gastric cancer	Up to 6 weeks from study start	Number of Participants diagnosed with advanced gastric cancer
Intersection over union (IOU) of gastric lesions	Up to 6 weeks from study start	A value between 0 and 1
Diagnosis of early gastric cancer	Up to 6 weeks from study start	Number of Participants diagnosed with early gastric cancer
Number of images analyzed for diagnosis of gastric cancer	Up to 6 weeks from study start	Number of upper gastrointestinal endoscopy images
Agreement on image and IOU based diagnosis of gastric cancer between AI and expert endoscopists	Up to 12 weeks from study start	Number of images and IOU value (between 0 and 1)

Trial Locations

Locations (1)

Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo; 🇯🇵 Tokyo, Japan