Diagnostic Modelling
Cross-source consensus on Diagnostic Modelling from 1 sources and 6 claims.
1 sources · 6 claims
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Highlighted claims
- The combined model achieved AUROC 0.802 with sensitivity 0.797 and specificity 0.679 at the optimal cut-off. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling
- The study built fundus-only, traditional-risk-factor and combined diagnostic models. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling
- The clinical-risk-factor model outperformed the fundus-only model, and the combined model performed best. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling
- The model was designed to identify current CAD in suspected-angina patients rather than predict future cardiovascular events. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling
- Bootstrap validation with 1000 iterations supported the combined model's stability. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling
- The logistic regression approach made the model more interpretable than many deep-learning retinal models because it used automated named retinal measurements. — Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling