Xu Xiaowen, Li Yang, Ding Ning, Zang Yukun, Sun Shanshan, Shen Gaoyu, Song Xiufeng
Department of Radiology, Qingdao University Affiliated Women and Children's Hospital, 6 Tongfu Road, Qingdao, Shandong 266034, China.
Department of Psychology, Qingdao University Affiliated Women and Children's Hospital, 6 Tongfu Road, Qingdao, Shandong 266034, China.
Psychiatry Res Neuroimaging. 2024 Dec;345:111901. doi: 10.1016/j.pscychresns.2024.111901. Epub 2024 Sep 16.
To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine learning (ML) methods in combination with structural magnetic resonance imaging (sMRI) features.
A total of 60 ASD children and 48 age- and sex-matched typically developing (TD) children were prospectively enrolled from January 2023 to April 2024. All subjects were scanned using 3D-T1 sequences. Automated brain segmentation techniques were utilized to obtain the standardized volume of each brain structure (the ratio of the absolute volume of brain structure to the whole brain volume). The standardized volumes of each brain structure in the two groups were statistically compared, and the volume data of brain areas with significant differences were combined with ML methods to diagnose and predict ASD patients.
Compared with the TD group, the volumes of the right lateral orbitofrontal cortex, right medial orbitofrontal cortex, right pars opercularis, right pars triangularis, left hippocampus, bilateral parahippocampal gyrus, left fusiform gyrus, right superior temporal gyrus, bilateral insula, bilateral inferior parietal cortex, right precuneus cortex, bilateral putamen, left pallidum, and right thalamus were significantly increased in the ASD group (P< 0.05). Among six ML algorithms, support vector machine (SVM) and adaboost (AB) had better performance in differentiating subjects with ASD from those TD children, with their average area under curve (AUC) reaching 0.91 and 0.92, respectively.
Automatic brain segmentation technology based on artificial intelligence can rapidly and directly measure and display the volume of brain structures in children with autism spectrum disorder and typically developing children. Children with ASD show abnormalities in multiple brain structures, and when paired with sMRI features, ML algorithms perform well in the diagnosis of ASD.
利用人工智能自动脑分割技术探索中国自闭症谱系障碍（ASD）儿童的脑结构特征，并结合结构磁共振成像（sMRI）特征，采用机器学习（ML）方法诊断ASD儿童。
2023年1月至2024年4月前瞻性纳入60例ASD儿童和48例年龄及性别匹配的发育正常（TD）儿童。所有受试者均采用3D-T1序列进行扫描。利用自动脑分割技术获取每个脑结构的标准化体积（脑结构绝对体积与全脑体积之比）。对两组中每个脑结构的标准化体积进行统计学比较，并将差异有统计学意义的脑区体积数据与ML方法相结合，以诊断和预测ASD患者。
与TD组相比，ASD组右侧眶额外侧皮质、右侧眶额内侧皮质、右侧 opercularis 部、右侧 triangularis 部、左侧海马体、双侧海马旁回、左侧梭状回、右侧颞上回、双侧脑岛、双侧顶下皮质、右侧楔前皮质、双侧壳核、左侧苍白球和右侧丘脑体积显著增加（P<0.05）。在六种ML算法中，支持向量机（SVM）和自适应增强（AB）在区分ASD受试者与TD儿童方面表现更好，其平均曲线下面积（AUC）分别达到0.91和0.92。
基于人工智能的自动脑分割技术能够快速、直接地测量和显示自闭症谱系障碍儿童和发育正常儿童的脑结构体积。ASD儿童存在多个脑结构异常，ML算法结合sMRI特征在ASD诊断中表现良好。
