Magnetic Resonance Imaging in Cerebral Small Vessel Disease‐Related Depression: From Visual Scoring to Artificial Intelligence
Chaofang Lei, Jiaxu Chen, Yilong WangABSTRACT
Background
Cerebral small vessel disease (CSVD) is the key pathological basis of vascular depression. The precise identification of its neuroimaging markers is of core value for early diagnosis, elucidation of its pathological mechanism, and individualized treatment. Recent advances in magnetic resonance imaging (MRI) and artificial intelligence (AI) have enabled automated, high‐throughput characterization of CSVD‐related brain lesions. However, the translation of these technical advances into clinical tools for depression‐specific prediction and classification remains at an early stage.
Results and Conclusion
This manuscript aims to summarize the application of traditional visual scoring systems in assessing the burden of CSVD and its association with depressive symptoms. Review the current status of imaging and AI research on CSVD‐related depression. To provide a direction for the development of more precise and efficient imaging diagnostic tools for the future, and ultimately promote the practical application and utilization of precision medicine in the field of CSVD‐related depression.