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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
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