Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant’s brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers’ output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT’s output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.

神经精神疾病的诊断基于行为标准，这使得诊断具有挑战性。需要诸如神经影像学等客观生物标志物，并且当与机器学习相结合时，可以辅助诊断决策并提高其可靠性。分析了 64 名精神分裂症、36 名自闭症谱系障碍 (ASD) 和 106 名正常发育的个体。FreeSurfer 用于从参与者的脑部扫描中获取数据。使用六个分类器对受试者进行分类。随后，26 名精神病超高风险 (UHR) 和 17 名首发精神病 (FEP) 受试者通过训练有素的分类器运行。最后，患者组的分类器输出与其临床严重程度相关。所有六个分类器在区分主题组方面表现相对较好，特别是支持向量机（SVM）和逻辑回归（LR）。皮质厚度和皮质下体积特征组对分类最有用。LR和SVM与ASD的临床指标高度一致。当 UHR 和 FEP 组与训练有素的分类器一起运行时，大多数病例被归类为精神分裂症，没有一个被归类为 ASD。总的来说，SVM 和 LR 是表现最好的分类器。与表面积相比，皮质厚度和皮质下体积对分类最有用。LR、SVM 和 DT 的输出具有临床信息。训练有素的分类器能够帮助预测 UHR 和 FEP 个体的诊断类别。LR和SVM与ASD的临床指标高度一致。当 UHR 和 FEP 组与训练有素的分类器一起运行时，大多数病例被归类为精神分裂症，没有一个被归类为 ASD。总的来说，SVM 和 LR 是表现最好的分类器。与表面积相比，皮质厚度和皮质下体积对分类最有用。LR、SVM 和 DT 的输出具有临床信息。训练有素的分类器能够帮助预测 UHR 和 FEP 个体的诊断类别。LR和SVM与ASD的临床指标高度一致。当 UHR 和 FEP 组与训练有素的分类器一起运行时，大多数病例被归类为精神分裂症，没有一个被归类为 ASD。总的来说，SVM 和 LR 是表现最好的分类器。与表面积相比，皮质厚度和皮质下体积对分类最有用。LR、SVM 和 DT 的输出具有临床信息。训练有素的分类器能够帮助预测 UHR 和 FEP 个体的诊断类别。与表面积相比。LR、SVM 和 DT 的输出具有临床信息。训练有素的分类器能够帮助预测 UHR 和 FEP 个体的诊断类别。与表面积相比。LR、SVM 和 DT 的输出具有临床信息。训练有素的分类器能够帮助预测 UHR 和 FEP 个体的诊断类别。