Neurodevelopmental disorders (NDDs) are a highly prevalent group of disorders and represent strong clinical behavioral similarities, and that make it very challenging for accurate identification of different NDDs such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Moreover, there is no reliable physiological markers for NDDs diagnosis and it solely relies on psychological evaluation criteria. However, it is crucial to prevent misdiagnosis and underdiagnosis by intelligent assisted diagnosis, which is closely related to the follow-up corresponding treatment. In order to relieve these issues, we propose a novel open set recognition framework for NDDs screening and detection, which is the first application of open set recognition in this field. It combines auto encoder and adversarial reciprocal points open set recognition to accurately identify known classes as well as recognize classes never encountered. And considering the strong similarities between different subjects, we present a joint scaling method called MMS to distinguish unknown disorders. To validate the feasibility of our presented method, we design a reciprocal opposition experiment protocol on the hybrid datasets from Autism Brain Imaging Data Exchange I (ABIDE I) and THE ADHD-200 SAMPLE (ADHD-200) with 791 samples from four sites and the results demonstrate the superiority on various metrics. Our OpenNDD has achieved promising performance, where the accuracy is 77.38%, AUROC is 75.53% and the open set classification rate is as high as 59.43%.

翻译：神经发育障碍（NDDs）是一类高发性疾病，其临床表现具有高度行为相似性，这使得准确识别不同NDD（如自闭症谱系障碍（ASD）和注意缺陷多动障碍（ADHD））极具挑战性。此外，NDD诊断缺乏可靠的生理标志物，完全依赖心理评估标准。然而，通过智能辅助诊断预防误诊和漏诊至关重要，因为这直接影响后续的相应治疗。为解决这些问题，我们提出了一种新颖的开放集识别框架用于NDD筛查与检测，这是该领域中首次应用开放集识别技术。该框架结合自编码器与对抗性互惠点开放集识别，能够准确识别已知类别并识别从未见过的类别。考虑到不同受试者之间的高度相似性，我们提出了一种名为MMS的联合缩放方法以区分未知障碍。为验证所提方法的可行性，我们在来自自闭症脑成像数据交换库I（ABIDE I）和ADHD-200样本库（ADHD-200）的混合数据集上设计了互逆对抗实验方案，该数据集包含来自四个站点的791个样本，结果证明了该方法在各项指标上的优越性。我们的OpenNDD取得了令人满意的性能，准确率达到77.38%，AUROC为75.53%，开放集分类率高达59.43%。