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CompletedNCT03759756Updated Apr 28, 2020

Artificial Intelligence for Early Diagnosis of Esophageal Squamous Cell Carcinoma

An observational study in Artificial Intelligence, Optical Enhancement Endoscopy and Magnifying Endoscopy, sponsored by Shandong University. Completed at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2020-04-28.

Sponsored by Shandong University · Observational

Study type
Observational
Model
Other
Time perspective
Retrospective
Enrollment
119
Ages
18 Years and older
Sex
All
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Study summary

Esophageal squamous cell carcinoma is one of the most common malignant tumor of upper digestive tract. However, the detection rate and diagnosis accuracy of early esophageal squamous cell cancer is low. The aim of this study is to develop a computer-assisted diagnosis tool combining with optical magnifying endoscopy for early detection and accurate diagnosis of it.

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Conditions studied

  • Artificial Intelligence
  • Optical Enhancement Endoscopy
  • Magnifying Endoscopy
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In context

Carcinoma, Squamous Cell

1,772 studies on the registry are indexed under Carcinoma, Squamous Cell; 412 are open to participants now.

This study's enrollment of 119 is above the median of 100 across 268 observational studies indexed under Carcinoma, Squamous Cell.

Browse Carcinoma, Squamous Cell studies →

Lead sponsor

Shandong University is the lead sponsor of 284 studies on the registry; 59 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Consecutive patients who came to Qilu Hospital of Shandong University and received optical magnifying OE endoscopy examination

Inclusion criteria

  • high risk patients for esophageal cancer aged 18 years or older;
  • Histologically verified early esophageal squamous cell cancer.

Exclusion criteria

Exclusion Criteria:

  • patients whose images of esophagus not suitable for the training, validation and testing the computer-assist diagnosis tool.
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Study design

Observational model
Other
Time perspective
Retrospective
Enrollment
119 participants (actual)
Patient registry
No

Groups and cohorts

  • AI visible group

    the endoscopic novices analyzing the images can see the automatic diagnosis of AI during the process

    Other: AI presentation

  • AI invisible group

    the endoscopic novice analyzing the images can not see the automatic diagnosis of AI during the process

    Other: no AI presentation

Interventions

  • OtherAI presentation

    AI presentation means the automatic diagnosis information of AI and AI presentation means it is visible in the group.

  • Otherno AI presentation

    AI presentation means the automatic diagnosis information of AI and no AI presentation means it is invisible in the group.

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What researchers measure

Primary outcomes

  1. the diagnosis efficiency of the AI model

    the sensitivity, specificity and accuracy of the AI model

    Time frame: 12 months

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Study locations

1 site
  • Department of Gastroenterology, Qilu Hospital, Shandong University
    Jinan, Shandong 250012, China
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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Apr 28, 2020, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT03759756
Lead sponsor
Shandong University
Responsible party
Yanqing Li (Vice president of Qilu Hospital, Shandong University) — Principal investigator
First posted
Nov 30, 2018
Start date
Dec 1, 2018
Primary completion
Mar 1, 2020
Completion
Apr 1, 2020
Last update
Apr 28, 2020

Study contacts

Yanqing Li, PHD
principal investigator · Qilu Hospital, Shandong University

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Apr 2020. You cannot join it, but the record below documents what was studied.

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