Deep learning-based detection of cerebral microbleeds on 2D T2*-weighted GRE MRI: toward ARIA-H risk assessment in Alzheimer's treatment.

Background: Amyloid-related imaging abnormalities with hemorrhage (ARIA-H) are a key safety concern in anti-amyloid therapies for Alzheimer's disease, as they are radiologically indistinguishable from cerebral microbleeds (CMBs). Accurate detection of CMBs is therefore essential for both treatment eligibility assessment and post-treatment safety monitoring. However, manual identification on 2D T2*-weighted gradient-recalled echo (GRE) MRI is labor-intensive and subject to variability. Objective: To develop and validate an artificial intelligence (AI)-based model for automated CMB detection using only 2D T2*-weighted GRE MRI, which is widely used in clinical settings. Methods: We implemented a YOLOv11-based deep learning model, preceded by a novel multi-channel preprocessing pipeline that enhances CMB visibility. The model was trained and tested using a dataset of 758 participants, with expert consensus used as the reference standard. Results: Using the optimized basic preprocessing with super-resolution (BP + SR) pipeline, the model achieved a lesion-level sensitivity of 0.694, precision of 0.705, and F1-score of 0.699. In patient-level analysis for detecting elevated CMB burden (≥4), the system demonstrated sensitivity of 0.933 and specificity of 0.935, supporting reliable stratification of CMB severity. Regional analysis showed sensitivity of 0.625 for lobar CMBs and 0.627 for deep structures. Conclusion: This study demonstrates the feasibility of robust CMB detection using only 2D T2*-weighted GRE MRI. Based on current performance, we position this system as a decision-support tool for GRE-based CMB screening, in which lesion-level detections may be aggregated to inform patient-level CMB burden relevant to ARIA-H risk stratification, while final ARIA grading and clinical decisions require expert neuroradiological confirmation.