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Updated: Jan 31, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
An abbreviated version of Silhouettes test: a brief validated mild cognitive impairment screening tool
Lin Huang1, Ke-Liang Chen1, Bi-Ying Lin1
1Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Objectives:
To revise an abbreviated version of the Silhouettes subtest of the Visual Object and Space Perception (VOSP) battery in order to recognize mild cognitive impairment (MCI) and determine the optimal cutoffs to differentiate among cognitively normal controls (NC), MCI, and Alzheimer's Disease (AD) in the Chinese elderly.
Design:
A cross-sectional validation study.
Setting:
Huashan Hospital, Shanghai, China.
Subjects:
A total of 591 participants: Individuals with MCI (n = 211), AD (n = 139) and NC (n = 241) were recruited from the Memory Clinic, Huashan Hospital, Shanghai, China.
Methods:
Baseline neuropsychological battery (including VOSP) scores were collected from firsthand data. An abbreviated version of silhouettes test (Silhouettes-A) was revised from the original English version more suitable for the elderly, including eight silhouettes of animals and seven silhouettes of inanimate objects, with a score ranging from 0 to 15.
Results:
Silhouettes-A was an effective test to screen MCI in the Chinese elderly with good sensitivity and specificity, similar to the Montreal cognitive assessment and superior to other single tests reflecting language, spatial, or executive function. However, it had no advantage in distinguishing MCI from AD. The corresponding optimal cutoff scores of Silhouettes-A were 10 for screening MCI and 8 for AD.
Conclusion:
Silhouettes-A is a quick, simple, sensitive, and dependable cognitive test to distinguish among NC, MCI, and AD patients.
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