Early detection of Alzheimer’s via machine learning with multi-modal data

Danika Gupta

doi:10.1117/12.2641481

3 October 2022 Early detection of Alzheimer’s via machine learning with multi-modal data

Danika Gupta

Author Affiliations +

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270O (2022) https://doi.org/10.1117/12.2641481
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Alzheimer’s is a chronic disease that impairs an individual’s cognitive ability to carry out daily actions. According to the World Health Organization, Alzheimer’s and other dementias rank 7th in the top causes of death worldwide. The US Centers for Disease Control (CDC) estimates that the number of Americans with Alzheimer’s will triple and reach 14 million in 2060. Our research explores Multi-Modal AI ensemble methods that combine MRI (Magnetic Resonance Imaging) scans with image-featurized brain volumetrics (Freesurfer) and clinician notes to effectively forecast Alzheimer’s. We used the OASIS3 dataset, a longitudinal study which tracked 1098 patients over thirty years and generated a total of 2168 MR sessions and over 6000 clinician visit notes. We evaluated the Convolutional Neural Networks (CNN) ResNet101, ResNet152 and ResNet200 for forecasting Alzheimer’s from the MRI scans. We then combined the MRI forecasts with Machine Learning techniques (Random Forest and K Nearest Neighbors) for forecasts based on Freesurfer featurized brain volumetrics and additional clinical data. The individual forecasts were combined using four ensemble methods (majority voting, aggressive which required only one modality, conservative which required all three and conditional where clinician notes took priority) to forecast an individual’s eventual Clinical Dementia Rating (CDR). The final results showed that a patient’s Alzheimer’s CDR can be forecasted early with an accuracy of over 94%, and that harmful False Negatives can be reduced by 2x-15x depending on the ensemble method chosen. These methods can assist in the early treatment of susceptible patients.

Conference Presentation

Citation Download Citation

Danika Gupta "Early detection of Alzheimer’s via machine learning with multi-modal data", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270O (3 October 2022); https://doi.org/10.1117/12.2641481

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