Core Viewpoint - The article discusses the development of a deep learning system called DeepRETStroke, which utilizes retinal images to detect silent brain infarction (SBI) and predict stroke risk, providing a cost-effective and non-invasive method for identifying high-risk populations [2][3][12]. Summary by Sections Traditional Stroke Risk Assessment - Traditional stroke risk assessments rely on clinical risk factors primarily from self-reported data, which have shown limited accuracy in identifying high-risk individuals, with consistency indices ranging from 0.58 to 0.73 [2]. Development of DeepRETStroke - The research team developed DeepRETStroke, a deep learning system that detects SBI and predicts stroke risk using only retinal images, eliminating the need for brain imaging [3][12]. Importance of Detecting SBI - SBI affects nearly 20% of the general population, indicating potential ischemic cerebrovascular disease and an increased risk of future strokes. Identifying SBI can help in better risk stratification and management of patients [6][12]. Limitations of Current Imaging Techniques - Current imaging techniques like MRI and CT for detecting SBI are impractical and costly for general screening, highlighting the need for simpler and more economical detection methods [7][8]. Advancements in Retinal Imaging - Recent advancements in medical imaging and deep learning emphasize the retina as a unique window to observe the brain, with retinal vessels sharing similarities with cerebral vessels, allowing for non-invasive early detection of cerebrovascular changes [8]. Research Methodology - The study involved three phases: pre-training DeepRETStroke with 895,640 retinal images, validating the system with 213,762 images from multiple countries, and conducting a real-world proof-of-concept study [9]. Performance Metrics - DeepRETStroke demonstrated strong performance in predicting new strokes with an area under the curve (AUC) of 0.901 and for recurrent strokes with an AUC of 0.769, showing consistent results across various datasets [9]. Real-World Validation - A prospective study involving 218 stroke patients showed that DeepRETStroke could stratify stroke risk effectively, leading to an 82.44% reduction in recurrent stroke events through appropriate interventions [10][12].