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使用机器学习以基于眼睛追踪技术诊断自闭症.

Ameera S Jaradat1, Mohammad Wedyan1, Saja Alomari1

  • 1Computer Science Department, Yarmouk University, Irbid 21163, Jordan.

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概括
此摘要是机器生成的。

早期诊断自闭症谱系障碍 (ASD) 是至关重要的. 这项研究利用眼睛跟踪数据和人工智能,在识别自闭症方面实现了高准确度,使得早期干预成为可能.

关键词:
诊断ASD诊断ASD诊断ASD诊断ASD诊断ASD诊断ASD诊断移动网络 (MobileNet) 是一个移动网络.深度学习是一种深度学习.混合学习是混合学习.图像的分类图像的分类.图像处理是图像处理的过程.机器学习是机器学习.堆叠组合学习学习 堆叠组合学习

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科学领域:

  • 神经发育障碍 神经发育障碍
  • 医疗保健中的人工智能
  • 生物医学数据分析

背景情况:

  • 早期诊断自闭症谱系障碍 (ASD) 对有效干预至关重要.
  • 目前的诊断方法通常依赖于三岁后的临床观察,延迟了关键的早期支持.
  • 需要更有效和更早的诊断方法来诊断ASD.

研究的目的:

  • 开发一种更早,更有效的诊断自闭症谱系障碍 (ASD) 的方法.
  • 为了评估人工智能模型的有效性,使用眼睛跟踪数据集来诊断ASD.
  • 使用眼睛跟踪数据识别自闭症得分.

主要方法:

  • 使用了Eye Gaze修复地图数据集和眼睛跟踪扫描路径数据集 (ETSDS) 进行ASD诊断.
  • 采用混合人工智能模型来分析眼睛跟踪数据.
  • 专门使用ETSDS的一个子集来识别自闭症分数.

主要成果:

  • 混合模型在Eye Gaze修复地图数据集上实现了96.1%的高准确率,在ETSDS上达到98.0%.
  • 在ETSDS上实现了98.1%的准确率,用于识别自闭症分数.
  • 与之前的研究相比,拟议的方法显示出更高的性能.

结论:

  • 人工智能技术对于诊断疾病,包括自闭症谱系障碍 (ASD) 是有效的.
  • 该研究强调了眼睛跟踪数据分析在早期发现自闭症方面的潜力.
  • 进一步研究用于疾病诊断的先进AI技术是有必要的.