Clock drawing test with convolutional neural networks to discriminate mild cognitive impairment

초록

Background and objectives: The Clock Drawing Test (CDT) is a tool to assess cognitive function. Despite its usefulness, its interpretation remains challenging, leading to a low reliability. The main objective of this study was to determine the feasibility of using the CDT with convolutional neural networks (CNNs) as a screening tool for amnestic type of mild cognitive impairment (a -MCI). Methods: A total of 177 CDT images were obtained from 103 healthy controls (HCs) and 74 patients with a -MCI. CNNs were trained to classify MCI based on the CDT images. To evaluate the performance of the CDT with CNNs, accuracy, sensitivity, speci ficity, precision, and f1 -score were calculated. To compare discriminant power, the area under the curve of the CDTwith CNNs and the Korean version of the Montreal Cognitive Assessment (MoCA-K) was calculated by the receiving operating characteristic curve analysis. Results: The CDTwith CNNs was more accurate in discriminating a -MCI (CDT with CNNs = 88.7%, MoCA- K = 81.8%). Furthermore, the CDT with CNNs could better discriminate a -MCI than the MoCA-K (AUC: CDTwith CNNs = 0.886, MoCA- K = 0.848). Conclusion: These results demonstrate the superiority of the CDT with CNNs to the MoCA-K for distinguishing a -MCI from HCs. The CDTwith CNNs could be a surrogate for a conventional screening tool for a -MCI. (c) 2024 Sociedad Espa & ntilde;ola de Psiquiatr & iacute;a y Salud Mental. Published by Elsevier Espa & ntilde;a, S.L.U. All rights reserved.

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