Cardiometabolic biomarker prediction based on retinal fundus image

Diagnosing common noncommunicable diseases, such as cardiovascular disease and diabetes, typically relies on blood sample analysis for biomarker measurement. This process is invasive, time-consuming, and relatively expensive. To address these limitations, deep learning methods were leveraged to estimate common cardiometabolic biomarkers using retinal fundus (RF) images. The study utilized 15,802 RF images from 5,653 participants in the Qatar Biobank (QBB), leading to the development of 19 deep-learning models to estimate biomarkers across seven categories: demographics and body composition, blood pressure, lipid profile, blood profile, hormones, kidney function, and metabolites. The proposed model outperformed existing models for the QBB-specific cohort across all biomarkers, achieving higher R-squared (R²) values, lower mean absolute error (MAE), and higher area under the curve (AUC). The proposed model achieved excellent performance in demographic predictions with age (MAE: 2.56, R²: 0.93) and gender (Accuracy: 96%, AUC: 0.94). For cardiovascular markers, it showed moderate predictability with systolic blood pressure (MAE: 8.02, R²: 0.49) and diastolic blood pressure (MAE: 6.06, R²: 0.45). For metabolic markers, the model demonstrated varying performance, with hemoglobin showing strong prediction (MAE: 0.79, R²: 0.60) while lipid markers showed moderate performance (total cholesterol MAE: 0.63, R²: 0.29). For creatinine, a kidney function marker, we achieved the best results with MAE: 9.00, R²: 0.33. Stratified analyses revealed systematic performance variations across gender, age, and disease-specific subgroups, with better predictions in males, young-agers, and non-diabetic participants. External validation of the CAD group confirms the effect of age, gender, and disease on prediction results, suggesting the need for personalized background in consideration for developing AI models. This study presents a promising approach for non-invasive biomarker estimation using retinal images, potentially revolutionizing early intervention and treatment planning in healthcare.