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NCT04314180

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

Status unknown Last updated 19 March 2020
What this trial tests

trial testing Taking slit-lamp images in Anterior Segment Disorders in 300 participants. Status unknown.

Timeline
1 February 2020
Primary endpoint
1 July 2020
1 July 2020

Quick facts

Lead sponsorSun Yat-sen University
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment300
Start date1 February 2020
Primary completion1 July 2020
Estimated completion1 July 2020
Sites1 location across China

Drugs / interventions tested

Conditions studied

Sponsor

Sun Yat-sen University

Who can join

Eligibility, any sex, with Anterior Segment Disorders or Artificial Intelligence. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.

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Other Sun Yat-sen University trials

Trials by the same sponsor.

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