Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

November 19, 2019 updated by: Ryota Niikura, Tokyo University

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

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

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 Type

Interventional

Enrollment (Actual)

500

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Japan
      • Tokyo, Japan, 1138655
        • Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

20 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

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

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: 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.
Diagnostic test: 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.
Active Comparator: Expert endoscopist 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.
Diagnostic test: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Per patient diagnosis of gastric cancer
Time Frame: Up to 6 weeks from study start
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
Time Frame: Up to 6 weeks from study start
Number of upper gastrointestinal endoscopy images
Up to 6 weeks from study start
Intersection over union (IOU) of gastric lesions
Time Frame: Up to 6 weeks from study start
A value between 0 and 1
Up to 6 weeks from study start
Diagnosis of advanced gastric cancer
Time Frame: Up to 6 weeks from study start
Number of Participants diagnosed with advanced gastric cancer
Up to 6 weeks from study start
Diagnosis of early gastric cancer
Time Frame: Up to 6 weeks from study start
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
Time Frame: Up to 12 weeks from study start
Number of images and IOU value (between 0 and 1)
Up to 12 weeks from study start

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Tokyo University

Investigators

  • Principal Investigator: Ryota Niikura, MD, Tokyo University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 1, 2019

Primary Completion (Actual)

October 1, 2019

Study Completion (Actual)

November 16, 2019

Study Registration Dates

First Submitted

July 19, 2019

First Submitted That Met QC Criteria

July 29, 2019

First Posted (Actual)

July 31, 2019

Study Record Updates

Last Update Posted (Actual)

November 20, 2019

Last Update Submitted That Met QC Criteria

November 19, 2019

Last Verified

November 1, 2019

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 11931-(1)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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