A team of researchers has developed a deep learning model that utilizes Diffusion Tensor Imaging (DTI) to predict vascular cognitive impairment (SVCI) from brain scans. The model, built on a DenseNet architecture, was trained on a dataset comprising 134 SVCI and 171 small vessel disease (SIVD) patients. It achieved an impressive accuracy of 90.2% on the internal test set and 92.6% on an external cohort, with an area under the ROC curve (AUC) of 0.951. The model effectively correlates predicted SVCI probabilities with actual neuropsychological scores, providing clinicians with a continuous measure of cognitive severity. By focusing on 11 specific white matter regions known to be affected by small vessel disease, the model allows for a nuanced assessment of cognitive risk across various domains. This innovative approach enables healthcare providers to stratify patients not just by overall diagnosis but by their risk of impairment in specific cognitive functions, all from a single DTI scan. The framework is particularly beneficial in clinical settings, requiring only standard DTI sequences without the need for extensive neuropsychological testing.