Jaradat Ameera S, Wedyan Mohammad, Alomari Saja, Barhoush Malek Mahmoud
Computer Science Department, Yarmouk University, Irbid 21163, Jordan.
Diagnostics (Basel). 2024 Dec 30;15(1):66. doi: 10.3390/diagnostics15010066.
One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child's abnormal behavior. The paper aims to find another way to diagnose autism that is effective and earlier than traditional methods of diagnosis. Therefore, we used the Eye Gaze fixes map dataset and Eye Tracking Scanpath dataset (ETSDS) to diagnose Autistic Spectrum Disorder (ASDs), while a subset of the ETSDS was used to recognize autism scores. The experimental results showed that the higher accuracy rate reached 96.1% and 98.0% for the hybrid model on Eye Gaze fixes map datasets and ETSDS, respectively. A higher accuracy rate was reached (98.1%) on the ETSDS used to recognize autism scores. Furthermore, the results showed the outperformer for the proposed method results compared to previous works. This confirms the effectiveness of using artificial intelligence techniques in diagnosing diseases in general and diagnosing autism, in addition to the need to increase research in the field of diagnosing diseases using advanced techniques.
自闭症的关键挑战之一是早期诊断。早期诊断能带来早期干预，从而改善病情，而非在未来使自闭症恶化。目前，自闭症诊断是在家长观察到孩子的异常行为后，孩子年满三岁以上，由医生或专家进行监测。本文旨在找到另一种诊断自闭症的方法，该方法比传统诊断方法更有效且更早。因此，我们使用眼注视点地图数据集和眼动追踪扫描路径数据集（ETSDS）来诊断自闭症谱系障碍（ASD），同时使用ETSDS的一个子集来识别自闭症分数。实验结果表明，混合模型在眼注视点地图数据集和ETSDS上的准确率分别达到了96.1%和98.0%。在用于识别自闭症分数的ETSDS上，准确率更高（98.1%）。此外，结果表明与先前的研究相比，该方法的效果更优。这证实了使用人工智能技术在一般疾病诊断以及自闭症诊断中的有效性，同时也表明需要增加在使用先进技术进行疾病诊断领域的研究。
