Clinical Trials Logo
  1. Home
  2. Artificial Intelligence
  3. NCT04314180
  4. Details
NCT04314180 Clinical Trial

Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis

Artificial Intelligence Clinical Trial

Official title:
Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis: A Clinical Trial

Clinical Trial Details — Status: Active, not recruiting

Administrative data
NCT number NCT04314180
Other study ID # IMAQUA2020-China-02
Secondary ID
Status Active, not recruiting
Phase
First received
Last updated
Start date February 1, 2020
Est. completion date July 1, 2020

Study information
Verified date March 2020
Source Sun Yat-sen University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Recruitment information / eligibility
Status Active, not recruiting
Enrollment 300
Est. completion date July 1, 2020
Est. primary completion date July 1, 2020
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group N/A and older
Eligibility Inclusion Criteria:

- Patients should be aware of the contents and signed for the informed consent.

Exclusion Criteria:

- 1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.

- 2. Patients who do not agree to sign informed consent.

Study Design

Related Conditions & MeSH terms

Intervention

Device:
• Taking slit-lamp images
The participant only needs to take several slit-lamp images as usual.

Locations
Country Name City State
China Zhongshan Ophthalmic Center, Sun Yat-sen University Guangzhou Guangdong

Sponsors (1)
Lead Sponsor Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome
Type Measure Description Time frame Safety issue
Primary Performance of artificial intelligence system for distinguish between good image quality and poor image quality Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values,accuracy 3 months
Secondary The comparison of the performance for previous artificial intelligence diagnostic system with slit-lamp images of different image quality Cohen's kappa coefficient, P value and other related statistic results 3 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