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This paper introduces AT-Attn, a novel temporal-aware multimodal framework designed to enhance the diagnosis of Alzheimer's disease by effectively integrating structural MRI data with longitudinal clinical information. The method employs Change-and-Time encoding, time-biased asymmetric cross-attention, and gated fusion to address the challenges posed by noisy or intermittently available MRI data. Evaluation on a cohort of 1,520 patients demonstrates that AT-Attn significantly outperforms both unimodal and naive multimodal fusion approaches, achieving an accuracy of 0.719 and a ROC-AUC of 0.873, indicating its potential for improving diagnostic assessments in clinical settings.
A temporal-aware fusion strategy boosts Alzheimer's diagnosis accuracy, leveraging noisy MRI data more effectively than traditional methods.
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable. We propose AT-Attn, a temporal-aware multimodal framework that combines Change-and-Time encoding, time-biased asymmetric cross-attention, and gated fusion to integrate MRI with longitudinal clinical information. We evaluate AT-Attn on an MRI-retained ADNI cohort of 1,520 patients using structural MRI, six cognitive-scale trajectories, and seven static clinical variables under patient-level five-fold cross-validation. The main asymmetric AT-Attn model achieves accuracy 0.719+/-0.024, macro F1 0.721+/-0.023, ROC-AUC 0.873+/-0.013, and PR-AUC 0.783+/-0.018, outperforming unimodal and naive multimodal fusion baselines while remaining competitive with strong tabular baselines. These results suggest that a temporal-aware and constrained fusion strategy can help structural MRI contribute clinically relevant complementary information for patient-level AD diagnosis support.
