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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Linguistic vulnerabilities in mild cognitive impairment: Evidence from the DTLA-Tr screening battery
Samet Tosun1, Fenise Selin Karalı1, Elif İkbal Eskioğlu2
1Department of Speech and Language Therapy, Biruni University, Istanbul, Turkey.
None:
Mild Cognitive Impairment (MCI) represents a transitional stage between normal aging and dementia and is associated with an increased risk of progression to Alzheimer's disease. Conventional cognitive screening tools provide limited sensitivity for detecting subtle language impairments that may emerge in the earliest phases of neurodegeneration. This study aimed to evaluate the discriminative validity of the Turkish adaptation of the Detection Test for Language Impairments in Adults and the Aged (DTLA-Tr) in identifying language deficits in individuals with MCI. The sample comprised 110 participants, including 55 individuals with MCI and 55 age-, education-, and gender-matched healthy controls. All participants completed the Montreal Cognitive Assessment Turkish version (MoCA-Tr), Boston Naming Test Turkish Version (BNT-Tr), and DTLA-Tr following a fixed administration order. Group differences were analyzed using non-parametric tests and mixed-effects modelling. Discriminative performance of the DTLA-Tr Total Score was evaluated using ROC curve analysis. Individuals with MCI demonstrated significantly lower performance across multiple DTLA-Tr subtests, particularly in Repetition, Verbal Fluency, Alpha Span, Reading, and Semantic Matching. The DTLA-Tr Total Score showed fair discriminative accuracy for MCI (AUC = .69). The optimal cut-off (≤82) yielded a sensitivity of .44 and specificity of .85, indicating stronger specificity than sensitivity. The findings suggest that DTLA-Tr is a culturally appropriate and clinically useful tool for detecting language-related cognitive decline in MCI. Although its sensitivity remains modest, its multidimensional structure captures linguistic impairment.
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