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Updated: Dec 20, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Rationale for Early Diagnosis of Mild Cognitive Impairment (MCI) Supported by Emerging Digital Technologies
M N Sabbagh1, M Boada, S Borson
1Marwan N. Sabbagh, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA, sabbagm@ccf.org; Tel.: (702) 483-6029; Fax: (702) 722-6584.
Abstract:
Disease-modifying pharmacotherapies for Alzheimer's Disease (AD) are currently in late-stage clinical development; once approved, new healthcare infrastructures and services, including primary healthcare, will be necessary to accommodate a huge demand for early and large-scale detection of AD. The increasing global accessibility of digital consumer electronics has opened up new prospects for early diagnosis and management of mild cognitive impairment (MCI) with particular regard to AD. This new wave of innovation has spurred research in both academia and industry, aimed at developing and validating a new "digital generation" of tools for the assessment of the cognitive performance. In light of this paradigm shift, an international working group (the Global Advisory Group on Future MCI Care Pathways) convened to elaborate on how digital tools may be optimally integrated in screening-diagnostic pathways of AD The working group developed consensus perspectives on new algorithms for large-scale screening, detection, and diagnosis of individuals with MCI within primary medical care delivery. In addition, the expert panel addressed operational aspects concerning the implementation of unsupervised at-home testing of cognitive performance. The ultimate intent of the working group's consensus perspectives is to provide guidance to developers of cognitive tests and tools to facilitate the transition toward globally accessible cognitive screening aimed at the early detection, diagnosis, and management of MCI due to AD.
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