Internet of things technology and edge computing have been applied increasingly in the field of medical treatment to solve the problem of imbalanced medical resources. To better diagnose Attention Deficit Hyperactivity Disorder (ADHD), we propose a new short-time diagnosis technology that can quickly analyze the functional magnetic resonance imaging (fMRI) of patients and assist doctors in remote diagnosis of patients. Different from current ADHD fMRI analysis methods, our method is fast and can reflect changes in the patients brain in different periods. This method can analyze the correlation between a small image segment and ADHD using streaming data and quantify it as a score. This score is trained and computed by the threshold-based EM-MI algorithm. Through the scores obtained by short-time analysis, we can distinguish healthy people from patients according to the probability of the image segment show a high correlation with ADHD. This method is tested by ADHD-200 data and has a good classification accuracy (70.4%). Besides, we make a visual display of the brain activities on healthy people and patients and find the difference is obvious. The above results show that our method can effectively help doctors in remote diagnosis of ADHD.

物联网技术和边缘计算已越来越多地应用于医疗领域，以解决医疗资源不平衡的问题。为了更好地诊断注意力缺陷多动障碍（ADHD），我们提出了一种新的短时诊断技术，可以快速分析患者的功能磁共振成像（fMRI），并协助医生对患者进行远程诊断。与当前的ADHD fMRI分析方法不同，我们的方法快速并且可以反映不同时期患者大脑的变化。该方法可以使用流数据分析小图像段与ADHD之间的相关性，并将其量化为得分。该分数由基于阈值的EM-MI算法训练和计算。通过短时分析获得的分数，我们可以根据图像片段与ADHD高度相关的概率将健康人与患者区分开。该方法经过ADHD-200数据测试，具有良好的分类准确性（70.4％）。此外，我们对健康人和患者的大脑活动进行了可视化显示，发现它们之间的区别是显而易见的。以上结果表明我们的方法可以有效地帮助医生进行多动症的远程诊断。