Scholarlink

Background <p>Alzheimer’s disease (AD) exhibits highly heterogeneous clinical courses. Early, accurate prediction and subgroup identification remain challenging due to reliance on single-modality data and coarse subtype schemes.</p> Objective <p>To develop and validate a multimodal framework that integrates 3D MRI and clinical indicators to (1) stratify patients into clinically meaningful progression subtypes and (2) forecast individual memory/cognitive trajectories at 6, 12, and 48 months.</p> Methods <p>Using ADNI-2 (n = 453), we extracted 3D T1-weighted MRI features via a pre-trained Med3D network and combined them with cognitive, functional, and genetic indicators. Non-negative matrix factorization projected patients into a two-dimensional progression space, and K-means defined three prognostic subgroups (“Low,” “Mild,” “Fast”). We compared several longitudinal architectures (CNN, Transformer, LSTM variants, ConvLSTM); interpretability was assessed with SHAP.</p> Results <p>Clustering metrics (Silhouette peak at k = 3) supported three distinct trajectories. Stacked LSTM led image-only prediction, while standard LSTM favored indicator-only data. Multimodal LSTM with attention achieved the lowest errors—MAE 0.196, 0.203, and 0.261 at 6, 12, and 48 months—alongside accuracies of 0.903, 0.845, and 0.791. SHAP highlighted memory- and language-related features as dominant contributors.</p> Conclusion <p>An interpretable, fully automated multimodal framework enables robust subgroup stratification and individualized cognitive forecasting up to four years, supporting personalized prognosis and targeted clinical decision-making.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Utilizing multimodal models to forecast Alzheimer's disease progression and clinical subtypes

  • 期刊论文

  • 发表日期：

    2025年12月8日

查看全文
  • Hao Ren,
  • Fengshi Jing,
  • Yue Zhou,
  • Zeyu Luo,
  • Xu Yu,
  • Wen Jin,
  • Lizhi Chen

摘要

Background

Alzheimer’s disease (AD) exhibits highly heterogeneous clinical courses. Early, accurate prediction and subgroup identification remain challenging due to reliance on single-modality data and coarse subtype schemes.

Objective

To develop and validate a multimodal framework that integrates 3D MRI and clinical indicators to (1) stratify patients into clinically meaningful progression subtypes and (2) forecast individual memory/cognitive trajectories at 6, 12, and 48 months.

Methods

Using ADNI-2 (n = 453), we extracted 3D T1-weighted MRI features via a pre-trained Med3D network and combined them with cognitive, functional, and genetic indicators. Non-negative matrix factorization projected patients into a two-dimensional progression space, and K-means defined three prognostic subgroups (“Low,” “Mild,” “Fast”). We compared several longitudinal architectures (CNN, Transformer, LSTM variants, ConvLSTM); interpretability was assessed with SHAP.

Results

Clustering metrics (Silhouette peak at k = 3) supported three distinct trajectories. Stacked LSTM led image-only prediction, while standard LSTM favored indicator-only data. Multimodal LSTM with attention achieved the lowest errors—MAE 0.196, 0.203, and 0.261 at 6, 12, and 48 months—alongside accuracies of 0.903, 0.845, and 0.791. SHAP highlighted memory- and language-related features as dominant contributors.

Conclusion

An interpretable, fully automated multimodal framework enables robust subgroup stratification and individualized cognitive forecasting up to four years, supporting personalized prognosis and targeted clinical decision-making.