PDF(Pubmed)
Abstract：
UNASSIGNED: Timely and accurate detection of Autism Spectrum Disorder (ASD) is essential for early intervention and improved patient outcomes. This study aims to harness the power of machine learning (ML) techniques to improve ASD detection by incorporating temporal eye-tracking data. We developed a novel ML model to leverage eye scan paths, sequences of distances of eye movement, and a sequence of fixation durations, enhancing the temporal aspect of the analysis for more effective ASD identification.
UNASSIGNED: We utilized a dataset of eye-tracking data without augmentation to train our ML model, which consists of a CNN-GRU-ANN architecture. The model was trained using gaze maps, the sequences of distances between eye fixations, and durations of fixations and saccades. Additionally, we employed a validation dataset to assess the model\'s performance and compare it with other works.
UNASSIGNED: Our ML model demonstrated superior performance in ASD detection compared to the VGG-16 model. By incorporating temporal information from eye-tracking data, our model achieved higher accuracy, precision, and recall. The novel addition of sequence-based features allowed our model to effectively distinguish between ASD and typically developing individuals, achieving an impressive precision value of 93.10% on the validation dataset.
UNASSIGNED: This study presents an ML-based approach to ASD detection by utilizing machine learning techniques and incorporating temporal eye-tracking data. Our findings highlight the potential of temporal analysis for improved ASD detection and provide a promising direction for further advancements in the field of eye-tracking-based diagnosis and intervention for neurodevelopmental disorders.
UNASSIGNED: We utilized a dataset of eye-tracking data without augmentation to train our ML model, which consists of a CNN-GRU-ANN architecture. The model was trained using gaze maps, the sequences of distances between eye fixations, and durations of fixations and saccades. Additionally, we employed a validation dataset to assess the model\'s performance and compare it with other works.
UNASSIGNED: Our ML model demonstrated superior performance in ASD detection compared to the VGG-16 model. By incorporating temporal information from eye-tracking data, our model achieved higher accuracy, precision, and recall. The novel addition of sequence-based features allowed our model to effectively distinguish between ASD and typically developing individuals, achieving an impressive precision value of 93.10% on the validation dataset.
UNASSIGNED: This study presents an ML-based approach to ASD detection by utilizing machine learning techniques and incorporating temporal eye-tracking data. Our findings highlight the potential of temporal analysis for improved ASD detection and provide a promising direction for further advancements in the field of eye-tracking-based diagnosis and intervention for neurodevelopmental disorders.
摘要：
■及时准确地检测自闭症谱系障碍（ASD）对于早期干预和改善患者预后至关重要。本研究旨在利用机器学习（ML）技术的强大功能，通过结合时间眼动跟踪数据来改善ASD检测。我们开发了一种新颖的机器学习模型来利用眼睛扫描路径，眼球运动的距离序列，和一系列固定持续时间，增强分析的时间方面，以更有效地识别ASD。
■我们利用了眼动追踪数据的数据集来训练我们的机器学习模型，由CNN-GRU-ANN架构组成。模型是用凝视图训练的，眼睛注视之间的距离序列，以及注视和扫视的持续时间。此外,我们使用了一个验证数据集来评估模型的性能，并将其与其他作品进行比较。
■与VGG-16模型相比，我们的ML模型在ASD检测中表现出卓越的性能。通过合并来自眼睛跟踪数据的时间信息，我们的模型实现了更高的精度，精度,和回忆。新添加的基于序列的特征允许我们的模型有效地区分ASD和典型的发展中的个人，在验证数据集上实现了93.10%的令人印象深刻的精度值。
■本研究提出了一种基于ML的ASD检测方法，该方法利用机器学习技术并结合时间眼动跟踪数据。我们的发现强调了时间分析在改善ASD检测方面的潜力，并为神经发育障碍的基于眼睛跟踪的诊断和干预领域的进一步发展提供了有希望的方向。
■我们利用了眼动追踪数据的数据集来训练我们的机器学习模型，由CNN-GRU-ANN架构组成。模型是用凝视图训练的，眼睛注视之间的距离序列，以及注视和扫视的持续时间。此外,我们使用了一个验证数据集来评估模型的性能，并将其与其他作品进行比较。
■与VGG-16模型相比，我们的ML模型在ASD检测中表现出卓越的性能。通过合并来自眼睛跟踪数据的时间信息，我们的模型实现了更高的精度，精度,和回忆。新添加的基于序列的特征允许我们的模型有效地区分ASD和典型的发展中的个人，在验证数据集上实现了93.10%的令人印象深刻的精度值。
■本研究提出了一种基于ML的ASD检测方法，该方法利用机器学习技术并结合时间眼动跟踪数据。我们的发现强调了时间分析在改善ASD检测方面的潜力，并为神经发育障碍的基于眼睛跟踪的诊断和干预领域的进一步发展提供了有希望的方向。
