This study is to build an multi-modal artificial intelligence ophthalmological imaging diagnostic system covering multi-level medical institutions. We are going to evaluate this system in an evidence-based medicine view, taking diabetic retinopathy as an example. And clinical diagnostic criteria will be made based on this multi-modal artificial intelligence imaging diagnostic system. The study is designed as a cross-sectional study involving 1,000 normal individuals, 1,000 diabetes patients without ocular complications, and 1,000 with diabetic ocular complications. Statistical analysis of the diagnostic sensitivity and specificity of the artificial intelligence system will be made, and ROC curve wil be draw.
Study Type
OBSERVATIONAL
Enrollment
3,000
Zhongshan Opthalmic Center
Guangzhou, Guangdong, China
RECRUITINGThe diagnostic sensitivity, specificity and ROC curve of the artificial intelligence(AI) system compared with reference standard (ophthalmologist).
Sensitivity: the percentage of diabetic retinopathy(DR) patients who are correctly identified as having the condition by AI. Sensitivity=True positive/(True positive+False negative). Specificity: the percentage of healthy people who are correctly identified as not having the condition by AI. Specificity=True negative /(True negative +False positive). The ROC curve was plotted with true positive rate (sensitivity) as the ordinate and false positive rate (1-specificity) as the abscissa.
Time frame: It will take 15~20min for each subject to take the exams.
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