El Hmimdi Alae Eddine, Kapoula Zoï
Orasis-Eye Analytics & Rehabilitation Research Group, Spinoff CNRS, 12 Rue Lacretelle, 75015 Paris, France.
Bioengineering (Basel). 2025 Jul 5;12(7):737. doi: 10.3390/bioengineering12070737.
This study investigates whether eye movement abnormalities can differentiate between distinct clinical annotations of dyslexia, attention deficit, or school learning difficulties in children. Utilizing a selection of saccade and vergence eye movement data from a large clinical dataset recorded across 20 European centers using the REMOBI and AIDEAL technologies, this research study focuses on individuals annotated with only one of the three annotations. The selected dataset includes 355 individuals for saccade tests and 454 for vergence tasks. Eye movement analysis was performed with AIDEAL software. Key parameters, such as amplitude, latency, duration, and velocity, are extracted and processed to remove outliers and standardize values. Machine learning models, including logistic regression, random forest, support vector machines, and neural networks, are trained using a GroupKFold strategy to ensure patient data are present in either the training or test set. Results from the machine learning models revealed that children annotated solely with dyslexia could be successfully identified based on their saccade and vergence eye movements, while identification of the other two categories was less distinct. Statistical evaluation using the Kruskal-Wallis test highlighted significant group mean differences in several saccade parameters, such as a velocity and latency, particularly for dyslexics relative to the other two groups. These findings suggest that specific terminology, such as "dyslexia", may capture unique eye movement patterns, underscoring the importance of eye movement analysis as a diagnostic tool for understanding the complexity of these conditions. This study emphasizes the potential of eye movement analysis in refining diagnostic precision and capturing the nuanced differences between dyslexia, attention deficits, and general learning difficulties.
本研究调查了眼动异常是否能够区分儿童阅读障碍、注意力缺陷或学校学习困难的不同临床注释。利用从20个欧洲中心使用REMOBI和AIDEAL技术记录的大型临床数据集中选取的一系列扫视和聚散眼动数据，本研究聚焦于仅被标注了三种注释之一的个体。所选数据集包括355名用于扫视测试的个体和454名用于聚散任务的个体。使用AIDEAL软件进行眼动分析。提取并处理诸如幅度、潜伏期、持续时间和速度等关键参数，以去除异常值并标准化数值。使用GroupKFold策略训练包括逻辑回归、随机森林、支持向量机和神经网络在内的机器学习模型，以确保患者数据出现在训练集或测试集中。机器学习模型的结果显示，仅被标注为阅读障碍的儿童能够基于其扫视和聚散眼动被成功识别，而另外两类的识别则不太明显。使用Kruskal-Wallis检验的统计评估突出了几个扫视参数（如速度和潜伏期）的显著组均值差异，特别是阅读障碍者相对于其他两组。这些发现表明，诸如“阅读障碍”这样的特定术语可能捕捉到独特的眼动模式，强调了眼动分析作为理解这些病症复杂性的诊断工具的重要性。本研究强调了眼动分析在提高诊断精度以及捕捉阅读障碍、注意力缺陷和一般学习困难之间细微差异方面的潜力。
