Identifying dementia neuropathology using low-burden clinical data.

INTRODUCTION

Identifying dementia neuropathology is critical for guiding effective therapies and clinical trials. To tackle this, we developed semi-supervised models for identifying neuropathology using low-burden data to improve generalizability.

METHODS

We defined low-burden data as being reasonably obtainable at a primary care setting. By using a semi-supervised learning paradigm, we can amplify the utility of low-burden data. We trained a clustering and a semi-supervised prediction model to yield clustering and prediction results for different neuropathology lesion types.

RESULTS

Our clustering model identified two clinically meaningful outlier groups that were either neuropathology-enriched or -scarce. We predicted neuropathology burden across different pathology types and found that using low-burden data over multiple clinical visits can predict neuropathology on par with using higher-burden data.

DISCUSSION

This work fills a critical gap in the field by using low-burden clinical data to predict neuropathology, thereby improving dementia screening, therapy, and targeted clinical trials.

HIGHLIGHTS

Clinical data are useful for neuropathology screening in future clinical trials. Novel application of semi-supervised learning for identifying neuropathology. Clustering model found groups with highly different neuropathology prevalence. Low-burden data can provide relatively accurate predictions of pathology load. Higher-burden, longitudinal data are most helpful for predicting vascular lesions.