Clinical Trials Logo
  1. Home
  2. Artificial Intelligence
  3. NCT03784209
  4. Details
NCT03784209 Clinical Trial

Automatic Real-time Diagnosis of Gastric Mucosal Disease Using pCLE With Artificial Intelligence

Artificial Intelligence Clinical Trial

Official title:
Automatic Real-time Diagnosis of Gastric Mucosal Disease Using Probe-based Confocal Laser Endomicroscopy With Artificial Intelligence

Clinical Trial Details — Status: Completed

Administrative data
NCT number NCT03784209
Other study ID # 2018SDU-QILU-12
Secondary ID
Status Completed
Phase
First received
Last updated
Start date July 1, 2018
Est. completion date September 29, 2021

Study information
Verified date March 2022
Source Shandong University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Probe-based confocal laser endomicroscopy (pCLE) is an endoscopic technique that enables real-time histological evaluation of gastric mucosal disease during ongoing endoscopy examination. However this requires much experience, which limits the application of pCLE. The investigators designed a computer-aided diagnosis program using deep neural network to make diagnosis automatically in pCLE examination and contrast its performance with endoscopists.

Recruitment information / eligibility
Status Completed
Enrollment 951
Est. completion date September 29, 2021
Est. primary completion date September 29, 2021
Accepts healthy volunteers No
Gender All
Age group 18 Years to 80 Years
Eligibility Inclusion Criteria: - aged between 18 and 80; - agree to give written informed consent. Exclusion Criteria: - Patients under conditions unsuitable for performing CLE including coagulopathy , impaired renal or hepatic function, pregnancy or breastfeeding, and known allergy to fluorescein sodium; - Inability to provide informed consent

Study Design

Related Conditions & MeSH terms

Intervention

Diagnostic Test:
• The diagnosis of Artificial Intelligence and endoscopist
When suspected lesion is observed using pCLE, endoscopist and AI will make a diagnosis independently. In addition, the endoscopist can not see the diagnosis of AI.

Locations
Country Name City State
China Endoscopic unit of Qilu Hospital Shandong University Jinan Shandong

Sponsors (1)
Lead Sponsor Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary The diagnosis efficiency of Artificial Intelligence The primary outcome is to test the diagnostic accuracy, sensitivity, specificity, PPV, NPV of the Artificial Intelligence for diagnosing gastric mucosal disease on real-time pCLE examination. 24 months
Secondary Contrast the diagnosis efficiency of Artificial Intelligence with endoscopists The secondary outcome is to compare the diagnosis efficiency (including diagnostic accuracy, sensitivity, specificity, PPV, NPV for diagnosing gastric mucosal disease on real-time pCLE examination) between Artificial Intelligence and endoscopists. 24 months
See also
  Status Clinical Trial Phase
Completed NCT04589078 - Polyp REcognition Assisted by a Device Interactive Characterization Tool - The PREDICT Study
Completed NCT03857438 - Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
Completed NCT04735055 - Artificial Intelligence Prediction for the Severity of Acute Pancreatitis
Not yet recruiting NCT05452993 - Screening for Diabetic Retinopathy in Pharmacies With Artificial Intelligence Enhanced Retinophotography N/A
Not yet recruiting NCT04337229 - Evaluation of Comfort Behavior Levels of Newborns With Artificial Intelligence Techniques N/A
Completed NCT05687318 - A Clinical Trial of the Effectiveness and Safety of Software Assisting Diagnose the Intestinal Polyp Digestive Endoscopy by Analysis of Colonoscopy Medical Images From Electronic Digestive Endoscopy Equipment N/A
Recruiting NCT06051682 - Optimization of the Diagnosis of Bone Fractures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor. N/A
Not yet recruiting NCT06039917 - Effect of the Automatic Surveillance System on Surveillance Rate of Patients With Gastric Premalignant Lesions N/A
Not yet recruiting NCT06362629 - AI App for Management of Atopic Dermatitis N/A
Recruiting NCT06059378 - Real-life Implementation of an AI-based Optical Diagnosis N/A
Recruiting NCT06164002 - A I in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With 2 Ceramic Materials N/A
Completed NCT05517889 - Repeatability and Stability of Healthy Skin Features on OCT
Completed NCT04816981 - AI-EBUS-Elastography for LN Staging N/A
Completed NCT05006092 - Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE) N/A
Recruiting NCT04535466 - Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics
Enrolling by invitation NCT04719117 - Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
Completed NCT04399590 - Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
Recruiting NCT04126265 - Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps N/A
Recruiting NCT06255808 - Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor
Recruiting NCT04131530 - Automatic Evaluation of Inflammation Activity in Ulcerative Colitis Using pCLE With Artificial Intelligence