Artificial Intelligence for Early Diagnosis of Esophageal Squamous Cell Carcinoma

NCT03759756 | Completed

• 1 other identifier: 2018SDU-QILU-2
• Study Type: observational
• Target: 119
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Artificial Intelligence; Optical Enhancement Endoscopy; Magnifying Endoscopy

Outcome Measures

Primary Outcomes (1)

• the diagnosis efficiency of the AI model

  the sensitivity, specificity and accuracy of the AI model

  12 months

Study Arms (2)

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

AI presentation OTHER

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

AI visible group

no AI presentation OTHER

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

AI invisible group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Shandong University: lead

Study Sites (1)

Department of Gastroenterology, Qilu Hospital, Shandong University

Jinan, Shandong, 250012, China

Location

Study Officials

• Yanqing Li, PHD

  Qilu Hospital of Shandong University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Vice president of Qilu Hospital

Study Record Dates

• First Submitted: November 28, 2018
• First Posted: November 30, 2018
• Study Start: December 1, 2018
• Primary Completion: March 1, 2020
• Study Completion: April 1, 2020
• Last Updated: April 28, 2020

Record last verified: 2020-04

Locations

CN (1)