Guigou Yanice, Hennequin Alexandre, Marchand Théo, Chebli Mouna, Pisella Lucie Isoline, Staccini Pascal, Douet Vannucci Vanessa
R&D Lab, O-Kidia, Nice, France.
Bioelectronic Lab, Ecole des Mines de Saint-Étienne, Gardanne, France.
Front Psychiatry. 2025 Mar 17;16:1466107. doi: 10.3389/fpsyt.2025.1466107. eCollection 2025.
Attention-deficit hyperactivity disorder (ADHD) occurs in 5.9% of youth, impacting their health and social conditions often across their lifespan. Currently, early diagnosis is constrained by clinical complexity and limited resources of professionals to conduct evaluations. Scalable methods for ADHD screening are thus needed. Recently, digital epidemiology and biometry, such as the visual, emotional, or digit pathway, have examined brain dysfunction in ADHD individuals. However, whether biometry can support screening for ADHD symptoms using a multimodal tech system is still unknown. The EPIDIA4Kids study aims to create objective measures, i.e., biometrics, that will provide a comprehensive transdiagnostic picture of individuals with ADHD, aligning with current evidence for comorbid presentations. Twenty-four children aged 7 to 12 years performed gamified tasks on an unmodified tablet using the XAI4Kids multimodal system, which allows extraction of biometrics (eye-, digit-, and emotion-tracking) from video and touch events using machine learning. Neuropsychological assessments and questionnaires were administered to provide ADHD-related measures. Each ADHD-related measure was evaluated with each biometric using linear mixed-effects models. In contrast to neuro-assessments, only two digit-tracking features had age and sex effects (p < 0.001) among the biometrics. Biometric constructs were predictors of working memory (p < 0.0001) and processing speed (p < 0.0001) and, to a lower extent, visuo-spatial skills (p = 0.003), inattention (p = 0.04), or achievement (p = 0.04), where multimodalities are crucial to capture several symptomatology dimensions. These results illustrate the potential of multimodality biometry gathered from a tablet as a viable and scalable transdiagnostic approach for screening ADHD symptomatology and improving accessibility to specialized professionals. Larger populations including clinically diagnosed ADHD will be needed for further validation.
注意缺陷多动障碍（ADHD）在5.9%的青少年中出现，常常会在他们的一生中影响其健康和社会状况。目前，早期诊断受到临床复杂性以及专业人员进行评估的资源有限的制约。因此，需要可扩展的ADHD筛查方法。最近，数字流行病学和生物特征识别技术，如视觉、情感或数字通路，已经对ADHD个体的脑功能障碍进行了研究。然而，生物特征识别技术是否能够使用多模态技术系统支持ADHD症状的筛查仍然未知。EPIDIA4Kids研究旨在创建客观测量方法，即生物特征识别技术，以提供ADHD个体的全面跨诊断情况，与当前共病表现的证据相一致。24名7至12岁的儿童使用XAI4Kids多模态系统在未改装的平板电脑上执行游戏化任务，该系统允许使用机器学习从视频和触摸事件中提取生物特征（眼睛、数字和情感追踪）。进行了神经心理学评估和问卷调查以提供与ADHD相关的测量。使用线性混合效应模型对每个与ADHD相关的测量与每种生物特征进行评估。与神经评估不同，在生物特征识别技术中，只有两个数字追踪特征具有年龄和性别效应（p < 0.001）。生物特征结构是工作记忆（p < 0.0001）和处理速度（p < 0.0001）的预测指标，在较低程度上也是视觉空间技能（p = 0.003）、注意力不集中（p = 0.04）或学业成绩（p = 0.04）的预测指标，其中多模态对于捕捉多个症状维度至关重要。这些结果说明了从平板电脑收集的多模态生物特征识别技术作为一种可行且可扩展的跨诊断方法用于筛查ADHD症状以及提高获得专业人员服务的可及性的潜力。需要纳入包括临床诊断为ADHD的更大规模人群进行进一步验证。
