Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests

Chen, Shuqing; Stromer, Daniel; Alabdalrahim, Harb Alnasser; Schwab, Stefan; Weih, Markus; Maier, Andreas

doi:10.1007/978-3-658-33198-6_69

Cited by 1 publication

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“…In similar approaches Amini et al ( 2021 ) and Youn et al ( 2021 ) use CNNs to predict cognitive impairment from the CDT and ROCF. Chen et al ( 2021 ) compare different CNN architectures like VGG16, ResNet-152 and DenseNet-121 for automatic dementia screening and scoring of the CDT. A combination of image-based analysis and sensor data is presented by Park and Lee ( 2021 ), who utilize CNN and U-Net architectures for classification and segmentation of hand-drawn CDT sketches.…”

Section: Related Workmentioning

confidence: 99%

Modeling Users' Cognitive Performance Using Digital Pen Features

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2022

Front. Artif. Intell.

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Digital pen features model characteristics of sketches and user behavior, and can be used for various supervised machine learning (ML) applications, such as multi-stroke sketch recognition and user modeling. In this work, we use a state-of-the-art set of more than 170 digital pen features, which we implement and make publicly available. The feature set is evaluated in the use case of analyzing paper-pencil-based neurocognitive assessments in the medical domain. Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The physician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the physician in real-time, thereby reducing manual scoring effort and producing objective reports. As part of our evaluation we examine how accurately different feature-based, supervised ML models can automatically score cognitive tests, with and without semantic content analysis. A series of ML-based sketch recognition experiments is conducted, evaluating 10 modern off-the-shelf ML classifiers (i.e., SVMs, Deep Learning, etc.) on a sketch data set which we recorded with 40 subjects from a geriatrics daycare clinic. In addition, an automated ML approach (AutoML) is explored for fine-tuning and optimizing classification performance on the data set, achieving superior recognition accuracies. Using standard ML techniques our feature set outperforms all previous approaches on the cognitive tests considered, i.e., the Clock Drawing Test, the Rey-Osterrieth Complex Figure Test, and the Trail Making Test, by automatically scoring cognitive tests with up to 87.5% accuracy in a binary classification task.

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