临床路径对早期阿尔茨海默病检测中的多模态深度学习至关重要
Clinical Pathways Matter for Multimodal Deep Learning in Early Alzheimers Disease Detection
浏览论文内容
中文总结 AI 辅助
该研究提出基于SigLIP的零样本多模态框架，结合结构MRI与常规临床变量，在ADNI队列中实现早期阿尔茨海默病风险分层，性能优于传统模型，且可扩展至纵向数据。
中文摘要 AI 辅助
识别阿尔茨海默病（AD）风险个体，尤其是临床前期和早期阶段的个体，仍具有挑战性。尽管基于结构磁共振成像（structural MRI）的深度学习方法有望作为非侵入性生物标志物，但现有的多模态模型需要针对特定任务进行训练，且依赖于临床实践中无法常规获取的生物标志物。在此，我们提出一种基于SigLIP的零样本多模态框架，该框架将结构MRI嵌入与常规收集的临床变量的文本嵌入相结合，用于临床前期或轻度认知障碍（MCI）阶段个体的早期AD风险分层。我们在来自ADNI队列的416名个体（年龄：72.73±6.7）中评估了该方法。我们使用未进行微调的SigLIP提取MRI和临床文本嵌入，将其组合为多模态表示，用于个体层面未来4年内的AD风险预测。我们进一步在单次就诊和两次就诊设置中比较了模型性能，以评估纵向信息的价值和框架的可扩展性。在单次就诊设置中，结合MRI嵌入与MMSE、年龄和性别，获得了0.91±0.02的AUC，优于基于CSF Aβ42的模型（AUC 0.73±0.08）和基于MMSE的模型（AUC 0.85±0.22）。在两次就诊设置中，性能保持或有所提升，支持该方法对纵向数据的可扩展性。这些发现表明，结构MRI与常规收集的临床变量的零样本多模态融合，无需针对特定任务进行重新训练，即可为早期AD风险分层提供一种实用且可扩展的策略。
英文摘要
Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep learning approaches based on structural MRI show promise as a non-invasive biomarker, existing multimodal models require task-specific training and depend on biomarkers that are not routinely available in clinical practice. Here, we propose a zero-shot multimodal framework based on SigLIP that combines structural MRI embeddings with text embeddings of routinely collected clinical variables for early AD risk stratification in individuals at preclinical or mild cognitive impairment (MCI) stages. We evaluated the approach in 416 individuals from the ADNI cohort (age: 72.73 +- 6.7). SigLIP was used without fine-tuning to extract MRI and clinical text embeddings, which were combined into multimodal representations for individual-level AD risk prediction within 4 years. We further compared the model performance in a single-visit and two-visit settings to assess the value of longitudinal information and framework scalability. In the single-visit setting, combining MRI embeddings with MMSE, age, and sex achieved an AUC of 0.91 +- 0.02, outperforming both a CSF A\b{eta}42-based model (AUC 0.73 +- 0.08) and an MMSE-based model (AUC 0.85 +- 0.22). In the two-visit setting, performance was maintained or improved, supporting the scalability of the approach to longitudinal data. These findings suggest that zero-shot multimodal fusion of structural MRI and routinely collected clinical variables may provide a practical and scalable strategy for early AD risk stratification without task-specific retraining.
