Skip to main content
OpenTrials
Completed

NCT Number: NCT04040374

Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Gastric Cancer Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Neoplasms Neoplasms by Site Stomach Diseases Stomach Neoplasms

Age range

20 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo

Tokyo, 1138655, Japan

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

AI-based diagnosis

Diagnostic Test

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.

The expert endoscopists-based diagnosis

Diagnostic Test

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 outcomes

  1. Per patient diagnosis of gastric cancer

    Time frame: Up to 6 weeks from study start

    Number of Participants

Secondary outcomes

  1. Number of images analyzed for diagnosis of gastric cancer

    Time frame: Up to 6 weeks from study start

    Number of upper gastrointestinal endoscopy images

  2. Intersection over union (IOU) of gastric lesions

    Time frame: Up to 6 weeks from study start

    A value between 0 and 1

  3. Diagnosis of advanced gastric cancer

    Time frame: Up to 6 weeks from study start

    Number of Participants diagnosed with advanced gastric cancer

  4. Diagnosis of early gastric cancer

    Time frame: Up to 6 weeks from study start

    Number of Participants diagnosed with early gastric cancer

  5. 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)

Sponsors and collaborators

Lead sponsor

Tokyo University

Other

Registry information

Official study 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

Important dates

Study start
2019
Primary completion
2019
Study completion
2019
First posted
Jul 31, 2019
Registry last updated
Nov 20, 2019

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.