Uncertainty-driven modality selection for data-efficient prediction of Alzheimer’s disease

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder. Early prediction of the risk of converting to AD for individuals at pre-dementia stages such as Mild Cognitive Impairment (MCI) is important. This could provide an opportunity for early intervention to slow down disease progression before significant irreversible neurodegeneration occurs. Neuroimaging datasets of different modalities such as MRI and PET have shown great promise. However, different data modalities are associated with varying acquisition costs/levels of accessibility to patients. We propose a machine learning (ML) framework, namely Uncertainty-driven Modality Selection (UMoS), that allows for sequentially adding data modalities for each patient on an as-needed basis, while at the same time achieving high prediction accuracy as if all the modalities were used. UMoS provides a tool to assist clinicians in deciding what data modalities/diagnostic exams each patient needs. We apply UMoS to a real-world dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) based on demographic/clinical data, MRI, and PET. UMoS shows high accuracy for predicting MCI conversion to AD, which has no significant difference from models based on the simultaneous use of all the modalities for each patient. The benefit of UMoS is significant data efficiency accomplished by saving a large percentage of patients from needing to acquire more costly/less accessible data modalities, thus lessening the burden on patients and the healthcare system.