COMPASS: A Clinically-Optimized Multimodal Prediction Architecture with Survival Strategy for AD Prognosis

Precise early diagnosis and progression prediction of Alzheimer's Disease (AD) are critical for optimizing clinical intervention. However, current methodologies often suffer from the passive utilization of clinical priors and rigid modal fusion strategies, failing to capture the heterogeneous variations of imaging biomarkers. Furthermore, predicting the precise time-to-conversion from Mild Cognitive Impairment (MCI) to AD remains a formidable challenge. To address these limitations, we propose COMPASS, a clinical-guided multi-modal framework that unifies diagnosis with a comprehensive survival strategy. Specifically, we instigate a paradigm shift to "clinical-prior-driven" learning by incorporating Clinical-Guided Spatial Attention (CGSA), which actively transforms clinical states into visual signals to modulate neural focus on pathological regions. To bridge the semantic gap between modalities, we introduce Reciprocal Semantic Interaction (RSI) via cross-attention, while a Disease-Stage-Aware Modal Fusion (DSAMF) module dynamically adjusts modal weights based on inferred disease severity to mimic clinical reasoning. Moreover, we specifically design a Dual-Head Joint Survival Risk and Time Prediction Network (DH-Net) to jointly perform quantitative conversion time prediction and patient risk stratification. Extensive experiments demonstrate that COMPASS outperforms state-of-the-art methods, achieving 83.19% accuracy in pMCI vs. sMCI classification, an MAE of 7.96 months for conversion time prediction, and a C-index of 0.819. Furthermore, we conducted in-depth neurobiological interpretability analyses, revealing right hippocampal dominance and synergistic regional impairment patterns, thereby providing new biological insights for early AD diagnosis and subtype identification.