Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring

Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitor...

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Published inFrontiers in digital health Vol. 6; p. 1265846
Main Authors Milner, Tracy, Brown, Matthew R G, Jones, Chelsea, Leung, Ada W S, Brémault-Phillips, Suzette
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 06.03.2024
Subjects
biomarkers
clinical
cognitive
digital
Digital Health
MCI
phenotypes
digital
MCI
clinical
phenotypes
cognitive
biomarkers
machine learning
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Summary:Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitoring MCI with traditional biomarkers is noteworthy. Digital biomarkers hold new promise in understanding MCI. Identifying digital biomarkers specifically for MCI, however, is complex. The biomarker profile for MCI is expected to be multidimensional with multiple phenotypes based on different etiologies. Advanced methodological approaches, such as high-dimensional statistics and deep machine learning, will be needed to build these multidimensional digital biomarker profiles for MCI. Comparing patients to these MCI phenotypes in clinical practice can assist clinicians in better determining etiologies, some of which may be reversible, and developing more precise care plans. Key considerations in developing reliable multidimensional digital biomarker profiles specific to an MCI population are also explored.
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Reviewed by: Meysam Asgari, Oregon Health and Science University, United States
Edited by: Tobias Kowatsch, University of Zurich, Switzerland
ISSN:2673-253X
2673-253X
DOI:10.3389/fdgth.2024.1265846