Handwriting assessment to distinguish comorbid learning difficulties from attention deficit hyperactivity disorder in <scp>C</scp>hinese adolescents: A case–control study

Li-Tsang, Cecilia W. P.; Li, Tim M. H.; Lau, Mandy S. W.; Ho, Choco H. Y.; Leung, Howard

doi:10.1002/mpr.1718

Cited by 15 publications

(17 citation statements)

References 33 publications

(33 reference statements)

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“…It is supported by the present study wherein there was no significant difference between the girls with and without ADHD, as females with ADHD are known to have fewer hyperactive/impulsive symptoms and more inattentive symptoms than males [19]. Other studies suggested that children with ADHD have a less appropriate speed of execution and more motor difficulties than children without ADHD [4, 20], but they did not distinguish the comorbid learning difficulties in ADHD that can cause lower writing speed [21]. Among the females, the handwriting of subjects with ADHD was never ascending compared to variable findings in the control group.…”

Section: Discussionsupporting

confidence: 45%

Handwriting in children with Attention Deficient Hyperactive Disorder: role of graphology

et al. 2019

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BackgroundHandwriting difficulties are common in children with attention deficient hyperactive disorder (ADHD). The aim of our study was to find distinctive characteristics of handwriting in children with ADHD by using graphology to analyze physical characteristics and patterns, and to evaluate whether graphological analysis is an effective ADHD diagnostic tool for clinicians.MethodThe cohort included 49 children aged 13–18 years attending a tertiary neurology and epilepsy center in 2016–2017; 22 had a previous DSM-IV/V diagnosis of ADHD. The children were asked to write a 10–12-line story in Hebrew on a blank sheet of paper with a blue pen over a 20-min period. The samples were analyzed by a licensed graphologist blinded to the clinical details of the children against a predetermined handwriting profile of individuals with ADHD. Each ADHD characteristic identified in each sample was accorded 1 point, up to a total of 15 points. Patients with a graphology score of 9–15 were considered to have ADHD.ResultsThere were 21 boys (43%) and 28 girls (57%) in the cohort; 15 boys (71.4%) and 7 girls (25%) had a DSM-IV/V diagnosis of ADHD. The mean graphology score was significantly higher in the children who had a DSM-IV/V diagnosis of ADHD than in the children who did not (9.61 + 3.49 vs. 5.79 + 4.01, p = 0.002, respectfully). Using a score of 9 as the cutoff, in the girls, graphology had a specificity of 80% (95% CI 59.2–92.8) and a of sensitivity 71.4% for predicting ADHD. Corresponding values in the boys were 75.0 and 76.2%.ConclusionThe handwriting of children with ADHD has specific characteristics. Graphology may serve as a clinically useful tool in the diagnosis of ADHD.

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Section: Discussionsupporting

confidence: 45%

Handwriting in children with Attention Deficient Hyperactive Disorder: role of graphology

et al. 2019

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“…Other studies suggested that children with ADHD have a less appropriate speed of execution and more motor difficulties than children without ADHD [4,20], but they did not distinguish the comorbid learning difficulties in ADHD that can cause lower writing speed [21]. Among the females, the handwriting of subjects with ADHD was never ascending compared to variable findings in the control group.…”

Section: Discussionmentioning

confidence: 94%

Handwriting in Children with Attention Deficient Hyperactive Disorder: Analyzing by graphology

Cohen

Cohen-Kroitoru

Halevy

et al. 2019

Preprint

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Objective: Handwriting difficulties are common in children with attention deficient hyperactive disorder (ADHD). The aim of our study was to find distinctive characteristics of handwriting in children with ADHD by using graphology to analyze physical characteristics and patterns, and to evaluate whether graphological analysis is an effective ADHD diagnostic tool for clinicians. Method: The study group included 21 (43%) males and 28 (57%) females, with 15 (71.4%) males and 7 (25%) females diagnosed with ADHD. A graphologist analyzed handwriting text from 49 patients, 22/49 previously diagnosed with ADHD, aged 13-18 years, in a randomized, single-blinded study. All study participants wrote a story in Hebrew in 10-12 lines, on a blank paper with a blue pen, during a period of twenty minutes. A licensed graphologist was given the papers, without details, for characterization analysis. The graphologist suggested a profile of a person with ADHD based on graphology theory for ADHD, and gave patients one point for each ADHD handwriting characteristic, up to 15 points. Patients with 9-15 points were considered to have ADHD, based on their graphology evaluation. Results The mean graphology score in the DSM based ADHD group was significantly higher than in the control group (9.61+3.49 vs. 5.79+4.01, p=0.002, respectfully). Using score of 0 as a cutoff point graphology-based ADHD score had an 80% specificity (95% CIs [59.2-92.8]), and a sensitivity of 71.4%. Conclusion: Handwriting in ADHD children and adolescence has specific characteristics, thus graphological analysis could be a useful tool to help clinicians in the diagnosis of ADHD.

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“…Specific learning disorder may affect handwriting in a way that can be visually distinguished (Li-Tsang, Lau, Ho, & Leung, 2018). Symptoms of specific leaning disorder may include impairment in written expression such as difficulty with spelling, grammar, punctuation, and organization (American Psychiatric Association, 2013).…”

Section: Deep Learning and Specific Learning Disorder Diagnosismentioning

confidence: 99%

“…Symptoms of specific leaning disorder may include impairment in written expression such as difficulty with spelling, grammar, punctuation, and organization (American Psychiatric Association, 2013). Handwriting performance and sensorimotor skills may be affected by specific leaning disorder (Li-Tsang et al, 2018). Students with specific learning disorder write at slower speed, and with greater variation in written character size (Lam, Au, Leung , & Li-Tsang, 2011).…”

Section: Deep Learning and Specific Learning Disorder Diagnosismentioning

confidence: 99%

Applying Deep Learning to Specific Learning Disorder Screening

Mor¹,

Dardeck

2020

Preprint

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Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.

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Handwriting assessment to distinguish comorbid learning difficulties from attention deficit hyperactivity disorder in Chinese adolescents: A case–control study

Abstract: Clinicians should be aware of the fundamental difference between the 2 disorders and make good use of handwriting assessment as a reference to deliver effective therapies and trainings.

Cited by 15 publications

References 33 publications

Handwriting in children with Attention Deficient Hyperactive Disorder: role of graphology

Handwriting in children with Attention Deficient Hyperactive Disorder: role of graphology

Handwriting in Children with Attention Deficient Hyperactive Disorder: Analyzing by graphology

Applying Deep Learning to Specific Learning Disorder Screening

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