- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04040374
Artificial Intelligence Versus Expert Endoscopists for Diagnosis of Gastric Cancer
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
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
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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Tokyo, Japan, 1138655
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
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.
Study Plan
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.
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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.
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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.
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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
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Number of Participants
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Up to 6 weeks from study start
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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
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Number of upper gastrointestinal endoscopy images
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Up to 6 weeks from study start
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Intersection over union (IOU) of gastric lesions
Time Frame: Up to 6 weeks from study start
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A value between 0 and 1
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Up to 6 weeks from study start
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Diagnosis of advanced gastric cancer
Time Frame: Up to 6 weeks from study start
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Number of Participants diagnosed with advanced gastric cancer
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Up to 6 weeks from study start
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Diagnosis of early gastric cancer
Time Frame: Up to 6 weeks from study start
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Number of Participants diagnosed with early gastric cancer
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Up to 6 weeks from study start
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Agreement on image and IOU based diagnosis of gastric cancer between AI and expert endoscopists
Time Frame: Up to 12 weeks from study start
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Number of images and IOU value (between 0 and 1)
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Up to 12 weeks from study start
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Ryota Niikura, MD, Tokyo University
Publications and helpful links
General Publications
- Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014 Sep 11;513(7517):202-9. doi: 10.1038/nature13480. Epub 2014 Jul 23.
- Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T, Ohnishi T, Fujishiro M, Matsuo K, Fujisaki J, Tada T. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer. 2018 Jul;21(4):653-660. doi: 10.1007/s10120-018-0793-2. Epub 2018 Jan 15.
- Niikura R, Aoki T, Shichijo S, Yamada A, Kawahara T, Kato Y, Hirata Y, Hayakawa Y, Suzuki N, Ochi M, Hirasawa T, Tada T, Kawai T, Koike K. Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy. 2022 Aug;54(8):780-784. doi: 10.1055/a-1660-6500. Epub 2022 May 4.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
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)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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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