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.
Study Type
OBSERVATIONAL
Enrollment
300
The participant only needs to take several slit-lamp images as usual.
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
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
Time frame: 3 months
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
Time frame: 3 months
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.