Khandan Khadem-Reza Zahra, Ahmadi Lashaki Reza, Shahram Mohammad Amin, Zare Hoda
Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Quant Imaging Med Surg. 2025 Jun 6;15(6):4935-4946. doi: 10.21037/qims-24-1402. Epub 2025 May 27.
Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by impairments in social interactions, communication, repetitive behaviors, and restricted interests. Magnetic resonance imaging (MRI) has been increasingly used to identify common patterns in individuals with autism for classification purposes. This study aims to develop an intelligent system for diagnosing ASD in children using resting-state functional magnetic resonance imaging (fMRI) and machine learning algorithms.
This study proposes a method for classifying children with ASD versus healthy control (HC) using resting-state fMRI. This study used images from 26 autistic children and 26 controls, aged 5 to 10 years. Image features were extracted from both groups, and the children with ASD were classified from the HCs using support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN), and artificial neural network (ANN) algorithms.
Our experimental results reveal that the proposed method accurately detects ASD using the ABIDE dataset and achieves accuracy of 88.46%, 73.07%, 82.69%, and 90.38% with SVM, RF, KNN and ANN algorithms, respectively.
Diagnosing autism through clinical evaluations is time-consuming and relies on expert expertise, highlighting the importance of intelligent diagnosis for this disorder. In this study, we developed an intelligent system that demonstrated high accuracy in ASD diagnosis using resting-state fMRI and machine learning techniques.
自闭症谱系障碍（ASD）是一种神经发育障碍，其特征在于社交互动、沟通、重复行为和兴趣受限方面存在缺陷。磁共振成像（MRI）越来越多地用于识别自闭症个体的常见模式以进行分类。本研究旨在开发一种使用静息态功能磁共振成像（fMRI）和机器学习算法诊断儿童ASD的智能系统。
本研究提出了一种使用静息态fMRI对患有ASD的儿童与健康对照（HC）进行分类的方法。本研究使用了26名年龄在5至10岁的自闭症儿童和26名对照的图像。从两组中提取图像特征，并使用支持向量机（SVM）、随机森林（RF）、K近邻（KNN）和人工神经网络（ANN）算法将ASD儿童与HC进行分类。
我们的实验结果表明，所提出的方法使用ABIDE数据集准确检测出ASD，使用SVM、RF、KNN和ANN算法分别达到了88.46%、73.07%、82.69%和90.38%的准确率。
通过临床评估诊断自闭症既耗时又依赖专家专业知识，凸显了对这种疾病进行智能诊断的重要性。在本研究中，我们开发了一种智能系统，该系统使用静息态fMRI和机器学习技术在ASD诊断中显示出高精度。
