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

Conditions
Artificial Intelligence
Anterior Segment Disorders
Registration Number
NCT04314180
Lead Sponsor
Sun Yat-sen University
Brief 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.

Detailed Description

Not available

Recruitment & Eligibility

Status
UNKNOWN
Sex
All
Target Recruitment
300
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.
    1. Patients who do not agree to sign informed consent.

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Primary Outcome Measures
NameTimeMethod
Performance of artificial intelligence system for distinguish between good image quality and poor image quality3 months

Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values,accuracy

Secondary Outcome Measures
NameTimeMethod
The comparison of the performance for previous artificial intelligence diagnostic system with slit-lamp images of different image quality3 months

Cohen's kappa coefficient, P value and other related statistic results

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen University

🇨🇳

Guangzhou, Guangdong, China

Zhongshan Ophthalmic Center, Sun Yat-sen University
🇨🇳Guangzhou, Guangdong, China
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