Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT
This work addresses the need for early and explainable subtype identification in Alzheimer's disease, which is crucial for improving treatment effectiveness, though it appears incremental by combining existing modalities with a new attention mechanism.
The authors tackled the problem of early-stage Alzheimer's disease subtype classification by proposing a multimodal framework using imaging, genetics, and clinical assessments, which outperformed baseline models and provided interpretable insights into cross-modal feature associations.
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease; yet its currently available treatments are limited to stopping disease progression. Moreover, effectiveness of these treatments is not guaranteed due to the heterogenetiy of the disease. Therefore, it is essential to be able to identify the disease subtypes at a very early stage. Current data driven approaches are able to classify the subtypes at later stages of AD or related disorders, but struggle when predicting at the asymptomatic or prodromal stage. Moreover, most existing models either lack explainability behind the classification or only use a single modality for the assessment, limiting scope of its analysis. Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages. Similarly, we build prompts and use large language models, such as ChatGPT, to interpret the findings of our model. In our framework, we propose a tri-modal co-attention mechanism (Tri-COAT) to explicitly learn the cross-modal feature associations. Our proposed model outperforms baseline models and provides insight into key cross-modal feature associations supported by known biological mechanisms.