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Updated: Jun 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Brain check-up: a structured approach diagnosing mild cognitive impairment in the primary care setting]
Lucas Wolski1, Ann-Kathrin Bopp2, Ann-Kathrin Schwientek3
1Klinik für Psychiatrie und Psychotherapie, Zentrum für kognitive Störungen, Klinikum rechts der Isar, Technische Universität München, Möhlstraße 26, 81675, München, Deutschland. Lucas.Wolski@tum.de.
Background:
The reason-related identification of mild cognitive impairment (MCI) in primary care is helpful to treat reversible causes or decelerate progression to dementia by optimal management of existing risk factors. In this process general practitioners are in a key position. The present feasibility study investigated the practicability of a diagnostic algorithm (brain check-up), comprising neuropsychological examinations, differential diagnoses and follow-up measures.
Method:
By means of a standardized questionnaire, the utilization and practicability of the brain check-up was surveyed in n = 37 medical practices of general practitioners and internists in Germany.
Results:
The brain check-up was performed by n = 37 physicians in 389 patients (66%). The main barriers to implementation included patients' fear of facing the results, the professionals' lack of time, and costs. Overall, 73% of the participants agreed that the brain check-up was practical in everyday treatment. Long waiting times for an appointment with a neurological/psychiatric specialist were perceived as a barrier for optimal care.
Conclusion:
The structured algorithm is convenient in physician's everyday practice and can contribute to identify patients with MCI more easily. Therefore, it appears to be a helpful tool in primary care. To achieve sustainability in everyday use, identified barriers need to be addressed during the implementation phase.
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