Handwriting legibility and fluency and their patterns of concurrent relations with spelling, graphomotor, and selective attention skills

Dysgraphia Differs Between Children With Developmental Coordination Disorder and/or Reading Disorder

Handwriting deficits, or dysgraphia, are present in several neurodevelopmental disorders. To investigate whether dysgraphia differs according to the associated disorder, we performed a detailed analysis of handwriting in a group of French children with developmental coordination disorders (DCD), reading disorder (RD), or comorbid RD and DCD. Handwriting deficits were investigated at the product (quality of the trace) and the process (movement that generates the trace) levels. Nineteen children with singular RD (among which eight with dysgraphia), 13 children with singular DCD (among which seven with dysgraphia), 16 children with comorbid RD+DCD (among which 11 with dysgraphia), and 20 typically developing children, age 7 to 12, performed the BHK (Brave Handwriting Kinder) test, a standardized assessment of handwriting, on a graphic tablet. Developmental coordination disorders primarily affected handwriting quality, while RD affected slowness and, to a lesser extent, quality. Children with RD, solely or comorbid with DCD, wasted time by lifting and stopping the pen when writing. The comorbidity added to but did not worsen, handwriting difficulties. These results reflect distinct motor impairments and/or strategies in children with DCD or RD. We identified subtypes of dysgraphia and advocated for a fine-grained analysis of the writing process and the assessment of motor and reading skills when studying dysgraphia.

In Silico Decoding of Parkinson's: Speech & Writing Analysis

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1-4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.