Abstract
Background
Neuropathologic changes are central for both understanding of the patients’ brains and making the definitive diagnosis of dementia‐related diseases. However, many of them are only obtainable post‐mortem. To make this information available while an individual is still alive, this study developed machine learning models that predict neuropathologic changes based on features obtainable during life.
Method
The multi‐site post‐mortem data (n∼5000) was obtained from National Alzheimer’s Coordinating Center. A multitask long‐short term memory‐based neural network architecture was developed with custom loss to predict the 13 neuropathologic changes from features during life measured longitudinally. The performance of the model was evaluated using entire unseen sites as test sets. Evaluation metrics include area under receiver’s operating curve (AUROC) and area under precision recall curve (AUPRC).
Result
The model was able to predict Alzheimer’s Disease neuropathologic changes (ADNC) and any Alzheimer’s‐related pathologies, such as Braak score, with great sensitivity, specificity, and precision (for example AUROC = 0.85; AUPRC = 0.96 for ADNC). Apart from these, the model can also predict hippocampal sclerosis accurately (AUROC = 0.79; AUPRC = 0.80) and Lewy Body disease at higher precision than clinician’s diagnosis. Model interpretation shows patterns in neuropsychological tests that are predictive of pathologic changes. Additionally, model error analysis revealed factors, such as resilient case ratios, that explain variation in performance between sites.
Conclusion
Patterns of measurable features during life can be used by machine learning to predict ADNC, hippocampal sclerosis, and to lesser extent Lewy Body.