A multimodal approach for ADHD with coexisting ASD detection for children.

Researchers tested a new way to identify children who have both ADHD and autism using special writing tablets and brain-monitoring devices. They had 28 children do handwriting tasks while measuring how they wrote and their brain activity. The technology was 96.4% accurate at telling the difference between children with ADHD/autism and typically developing children. This could help doctors make more accurate diagnoses since ADHD and autism symptoms often overlap.

This study developed a novel multimodal approach combining pen tablet and functional near-infrared spectroscopy (fNIRs) technology to identify children with ADHD and co-occurring autism spectrum disorder. Researchers compared handwriting dynamics and brain activity patterns between 13 children with ADHD/ASD and 15 typically developing children during two handwriting tasks (zigzag lines and periodic lines). Using machine learning algorithms, the approach achieved 96.4% classification accuracy for periodic line tasks, representing a 2% improvement over existing methods. The study suggests this technology could assist clinicians in providing more objective and accurate diagnosis of ADHD with coexisting ASD, addressing the challenge of overlapping symptoms between these conditions.

This technology-based approach could potentially support clinicians in diagnosing complex cases where ADHD and ASD co-occur. However, extensive validation studies with larger samples are needed before clinical implementation. The approach may complement but not replace comprehensive clinical assessment protocols.

Very small sample size (28 children total) limits generalizability. Study design unclear from abstract. No information about validation in clinical settings or comparison with standard diagnostic methods. Technology accessibility and cost considerations not addressed.

Identifying attention-deficit/hyperactivity disorder (ADHD) with coexisting autism spectrum disorder (ASD) for children is a challenging issue due to their complexity and overlapping symptoms. This study investigated from handwriting and executive function viewpoints simultaneously and developed a novel multimodal approach for identifying ADHD with coexisting ASD by fusing pen tablet and fNIRs data. This study used pen tablet and fNIRs device to compare writing dynamics and brain activity between ADHD with coexisting ASD and typically developing (TD) children during handwriting patterns. Two handwriting tasks including Zigzag line (ZL) and periodic lines (PL) were adopted for data collection.

Each task had two conditions: trace and predict. Various statistical features were derived from pen tablet and fNIRs data for each task. These features were then combined by fusing features derived from the trace and predict conditions to make two datasets (PL and ZL). The potentiality of these features was evaluated using Sequential Forward Floating Selection (SFFS)-based algorithm and support vector machine (SVM) was employed to evaluate the performance of ZL and PL tasks.

Data were collected from 13 ADHD children with co-occurring ASD and 15 TD children to evaluate the proposed ZL and PL tasks. The experimental results demonstrated that the proposed SFFS-SVM model achieved a classification accuracy of 96.4% for PL task. This is an improvement of more than 2% classification accuracy compared to existing studies. This approach shows promising potential and assisting physicians and clinicians to provide an objective and accurate diagnosis of ADHD with coexisting ASD.

This study proposes a novel approach that increase the detection rate and provides new insights for further research.