Clock Drawing Test Digit Recognition Using Static and Dynamic Features

Harbi, Zainab; Hicks, Yulia; Setchi, Rossitza

doi:10.1016/j.procs.2016.08.166

Cited by 21 publications

(20 citation statements)

References 16 publications

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“…Up to now, only little attention has been paid to amortization of the CDT-based screening or scoring of neurological diseases by means of deep neural networks. Harbi et al published work on digitizing and interpretation the handwritten CDT but not screening and/or scoring 22 , 23 . Another work investigates the digital clock-drawing test (DCDT) in contrast to the CDT where the author stated concerns about the assessment of the manual CDT scoring systems 24 .…”

Section: Introductionmentioning

confidence: 99%

“…Our solution skips the digitization steps proposed by Harbi et al. in 22 , 23 by classifying the CDT directly from the hand-drawn image. To the best of our knowledge, our work is the first study to investigate and compare these successful image classification DL networks for the evaluation of CDT tests.…”

Section: Introductionmentioning

confidence: 99%

“…We compared Visual Geometry Group (VGG16) 28 , Residual Networks(ResNet-152) 29 , and DenseNet (DenseNet-121) 30 , which provided the most competitive results in the image classification challenges, to classify CDT images for (1) screening and (2) scoring dementia. Our solution skips the digitization steps proposed by Harbi et al in 22,23 by classifying the CDT directly from the hand-drawn image. To the best of our knowledge, our work is the first study to investigate and compare these successful image classification DL networks for the evaluation of CDT tests.…”

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confidence: 99%

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Automatic dementia screening and scoring by applying deep learning on clock-drawing tests

Chen

Stromer

Alabdalrahim

et al. 2020

Sci Rep

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Dementia is one of the most common neurological syndromes in the world. Usually, diagnoses are made based on paper-and-pencil tests and scored depending on personal judgments of experts. This technique can introduce errors and has high inter-rater variability. To overcome these issues, we present an automatic assessment of the widely used paper-based clock-drawing test by means of deep neural networks. Our study includes a comparison of three modern architectures: VGG16, ResNet-152, and DenseNet-121. The dataset consisted of 1315 individuals. To deal with the limited amount of data, which also included several dementia types, we used optimization strategies for training the neural network. The outcome of our work is a standardized and digital estimation of the dementia screening result and severity level for an individual. We achieved accuracies of 96.65% for screening and up to 98.54% for scoring, overcoming the reported state-of-the-art as well as human accuracies. Due to the digital format, the paper-based test can be simply scanned by using a mobile device and then be evaluated also in areas where there is a staff shortage or where no clinical experts are available.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Automatic dementia screening and scoring by applying deep learning on clock-drawing tests

Chen

Stromer

Alabdalrahim

et al. 2020

Sci Rep

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“…CDT is considered an effective measure for early detection of dementia in the elderly. Studies like [15], [16] present image analysis based systems to analyze the CDT samples. Contrary to other tests which involve drawings and sketches, CDT involves recognition of handwritten digits which represents a mature area of research [17].…”

Section: Related Workmentioning

confidence: 99%

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

Nazar¹,

Moetesum

Ehsan

et al. 2017

2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)

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Graphomotor impressions are a product of complex cognitive, perceptual and motor skills and are widely used as psychometric tools for the diagnosis of a variety of neuro-psychological disorders. Apparent deformations in these responses are quantified as errors and are used are indicators of various conditions. Contrary to conventional assessment methods where manual analysis of impressions is carried out by trained clinicians, an automated scoring system is marked by several challenges. Prior to analysis, such computerized systems need to extract and recognize individual shapes drawn by subjects on a sheet of paper as an important pre-processing step. The aim of this study is to apply deep learning methods to recognize visual structures of interest produced by subjects. Experiments on figures of Bender Gestalt Test (BGT), a screening test for visuo-spatial and visuo-constructive disorders, produced by 120 subjects, demonstrate that deep feature representation brings significant improvements over classical approaches. The study is intended to be extended to discriminate coherent visual structures between produced figures and expected prototypes.

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“…16 In this work, we developed a novel multi-input deep learning model that integrates three different drawing tasks to perform an explainable MCI detection. Extending clock drawing-based detection with deep learning 8,9,[11][12][13] to include a cube-copying drawing and a trail-making task into model inputs, our convolutional neural networks (CNNs) equipped with the self-attention mechanism (multi-input Conv-Att) achieve an excellent classification performance. The multiinput Conv-Att model enjoys an improvement of 0.051, 0.241, and 0.095 gain on the average accuracy, F1-score, and area under the receiver operating characteristic curve (AUC), respectively, compared to those of a baseline CNN.…”

Section: Introductionmentioning

confidence: 99%

An Explainable Self-Attention Deep Neural Network for Detecting Mild Cognitive Impairment Using Multi-input Digital Drawing Tasks

Ruengchaijatuporn

Chatnuntawech

Teerapittayanon

et al. 2021

Preprint

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Mild cognitive impairment (MCI) is an early stage of age-inappropriate cognitive decline, which could develop into dementia – an untreatable neurodegenerative disorder. An early detection of MCI is a crucial step for timely prevention and intervention. To tackle this problem, recent studies have developed deep learning models to detect MCI and various types of dementia using data obtained from the classic clock-drawing test (CDT), a popular neuropsychological screening tool that can be easily and rapidly implemented for assessing cognitive impairments in an aging population. While these models succeed at distinguishing severe forms of dementia, it is still difficult to predict the early stage of the disease using the CDT data alone. Also, the state-of-the-art deep learning techniques still face the black-box challenges, making it questionable to implement them in the clinical setting. Here, we propose a novel deep learning modeling framework that incorporates data from multiple drawing tasks including the CDT, cube-copying, and trail-making tasks obtained from a digital platform. Using self-attention and soft-label methods, our model achieves much higher classification performance at detecting MCI compared to those of a well-established convolutional neural network model. Moreover, our model can highlight features of the MCI data that considerably deviate from those of the healthy aging population, offering accurate predictions for detecting MCI along with visual explanation that aids the interpretation of the deep learning model.

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Clock Drawing Test Digit Recognition Using Static and Dynamic Features

Cited by 21 publications

References 16 publications

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests

Automatic dementia screening and scoring by applying deep learning on clock-drawing tests

Classification of Graphomotor Impressions Using Convolutional Neural Networks: An Application to Automated Neuro-Psychological Screening Tests

An Explainable Self-Attention Deep Neural Network for Detecting Mild Cognitive Impairment Using Multi-input Digital Drawing Tasks

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