Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application
Multi-modal Imaging and Artificial Intelligence Diagnostic System for Multi-level Clinical Application
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.
Inclusion Criteria:
Group A(normal individuals): Meet all the items 1 ~ 5 below
Group B(diabetes patients without ocular complications): meet any of 1 to 3, and both 4 to 5 items
Group C(patients with diabetic ocular complications): meet with any of 1 to 3, and all 4 to 6 items
Exclusion Criteria:
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