Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

Study Overview
=================
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.

Official Title
-----------------
A Single-center, Retrospective, Open Label, Randomized Controlled Trial of Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer in Patients Who Underwent Upper Gastrointestinal Endoscopy

Conditions
-----------------
Gastric Cancer

Intervention / Treatment
-----------------
* Diagnostic Test: AI-based diagnosis
* Diagnostic Test: The expert endoscopists-based diagnosis


Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: Males or females aged ≥ 20 years who underwent upper gastrointestinal endoscopy at Tokyo University Hospital during 2018. Informed optout consent, obtained from each patient before completion of the study. Exclusion Criteria: Patients who underwent gastrectomy. Patients who underwent transnasal upper gastrointestinal endoscopy.

Ages Eligible for Study
-----------------
Minimum Age: 20 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AI-based diagnosis<br>• 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. | Diagnostic Test: AI-based diagnosis<br>* 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.<br>|
| Active Comparator: Expert endoscopist diagnosis<br>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. | Diagnostic Test: The expert endoscopists-based diagnosis<br>* 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.<br>|

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Per patient diagnosis of gastric cancer | Number of Participants | Up to 6 weeks from study start | 

Secondary Outcome Measures

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

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
artificial intelligence, gastric cancer