Anterior Segment Disorders, Artificial Intelligence
Conditions
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.
Interventions
DEVICETaking slit-lamp images
The participant only needs to take several slit-lamp images as usual.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of artificial intelligence system for distinguish between good image quality and poor image quality | 3 months | Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values,accuracy |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The comparison of the performance for previous artificial intelligence diagnostic system with slit-lamp images of different image quality | 3 months | Cohen's kappa coefficient, P value and other related statistic results |
Countries
China
Outcome results
None listed