Deep Learning with Imbalanced Data Identifies Early Mild Cognitive Impairment Amongst Typical Older Adults (Preprint) Article Swipe
Ruben Zapata
,
Alejandro Albizu
,
Cameron Perrin
,
Andrew O’Shea
,
Cheshire Hardcastle
,
Jessica N. Kraft
,
Gene E. Alexander
,
Michael Marsiske
,
Ronald A. Cohen
,
Adam J. Woods
,
Joseph M. Gullett
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.65411
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.65411
BACKGROUND Mild cognitive impairment is a transitional stage between normal cognitive aging and dementia. Identifying individuals at the preMCI stage, prior to the onset of mild cognitive decline, can be pivotal for early interventions aimed to reduce the progression neurodegeneration. OBJECTIVE The objective of this study is to develop convolutional neural networks trained on fluid attenuated inversion recovery magnetic resonance imaging for classification of preMCI in an imbalanced cohort of 350 participants. METHODS A DenseNet 264 convolutional neural network was trained on an imbalanced dataset of 350 participants with a dataset split into 64%, training, 16% validation, and 20% testing sets. Training was conducted with a batch size=70, epoch=200, processing images resized to a uniform dimension of 128×128×128 voxels, and optimizer=Adam. The optimization of our network was conducted using the Adam Optimizer with a learning rate of 10-3 and a weight decay of 5-4. Data augmentation strategy included Random affine transformation, random rotation across an axis, and random Gaussian noise on each image during training. RESULTS Mean age of the participants was 71.6 (SD 5.14); the average educational attainment was 16.3 years (SD 2.39). The mean MoCA score was 26.7 (SD 1.93). Our DenseNet mode achieved R2=0.146 and RMSE=0.569. CONCLUSIONS These findings underscore the potential of brain imaging and DL to identify this at-risk population, offering a promising tool for early detection and potential personalized preventative strategies against cognitive decline. Further research is warranted to improve upon the results to validate these findings in a larger, more diverse population.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.65411
- https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-65411-submitted.pdf
- OA Status
- gold
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4401691043
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https://openalex.org/W4401691043Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.65411Digital Object Identifier
- Title
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Deep Learning with Imbalanced Data Identifies Early Mild Cognitive Impairment Amongst Typical Older Adults (Preprint)Work title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-08-14Full publication date if available
- Authors
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Ruben Zapata, Alejandro Albizu, Cameron Perrin, Andrew O’Shea, Cheshire Hardcastle, Jessica N. Kraft, Gene E. Alexander, Michael Marsiske, Ronald A. Cohen, Adam J. Woods, Joseph M. GullettList of authors in order
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https://doi.org/10.2196/preprints.65411Publisher landing page
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https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-65411-submitted.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://s3.ca-central-1.amazonaws.com/assets.jmir.org/assets/preprints/preprint-65411-submitted.pdfDirect OA link when available
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Preprint, Cognitive impairment, Psychology, Cognition, Cognitive psychology, Artificial intelligence, Gerontology, Computer science, Medicine, World Wide Web, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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