Autistic spectrum disorder (ASD) is a neurodevelopment condition normally linked with substantial healthcare costs and time-consuming assessments where early detection of ASD traits can help limit the development of the condition. The existing conventional ASD screening methods contain a large number of items and are based on domain expert rules which may be criticized of being lengthy and subjective. More importantly, these methods use basic scoring functions to pinpoint to autistic traits rather intelligently learning patterns from cases and controls which can be more accurate and efficient. One promising solution to deal with the above issues and speed up ASD assessment referrals is to develop intelligent artificial intelligence screening methods that not only provide accurate pre-diagnostic classifications but also improve the efficiency and accessibility of the screening process. This paper proposes a new autism screening system that replaces the conventional scoring functions in classic screening methods with deep learning algorithms. The system is composed of a mobile application that provides the user interface capturing questionnaire data; an intelligent ASD detection web service that interfaces with a Convolutional Neural Network (CNN) trained with historical ASD cases; and a database that enables the CNN to learn new knowledge from future users of the system. The CNN classification method was evaluated against a large autism dataset consisting of adult, adolescent, child, and toddler cases and controls. The results obtained from the CNN were compared with other intelligent algorithms in which superior performance was achieved by the CNN. Particularly, the proposed CNN-based ASD classification system revealed higher accuracy, sensitivity, and specificity when compared with conventional screening methods. This indeed will be of high benefit for busy medical clinics and diagnosticians and could possibly be a new direction to change the way ASD diagnosis process is conducted in the future.

中文翻译：

---

自闭症AI：一种基于人工智能的新型自闭症筛查系统

自闭症谱系障碍（ASD）是一种神经发育疾病，通常与大量医疗保健费用和耗时的评估相关，其中早期发现ASD特质可以帮助限制该疾病的发展。现有的常规ASD筛选方法包含大量项目，并且基于领域专家规则，该规则可能会被批评为冗长和主观的。更重要的是，这些方法使用基本的评分功能来定位自闭症特征，而是从案例和控件中智能地学习模式，从而更加准确和高效。解决上述问题并加快ASD评估转介的一种有前途的解决方案是开发智能的人工智能筛查方法，该方法不仅可以提供准确的诊断前分类，还可以提高筛查过程的效率和可及性。本文提出了一种新的自闭症筛查系统，该系统用深度学习算法取代了经典筛查方法中的常规评分功能。该系统由一个移动应用程序组成，该应用程序提供了捕获问卷数据的用户界面。一种智能的ASD检测Web服务，该服务与经过历史ASD案例训练的卷积神经网络（CNN）交互；以及使CNN能够从系统的未来用户那里学习新知识的数据库。针对大型自闭症数据集（包括成人，青少年，儿童和幼儿病例和对照）评估了CNN分类方法。从CNN获得的结果与其他智能算法进行了比较，在其他智能算法中CNN获得了出色的性能。特别是，与传统的筛选方法相比，拟议的基于CNN的ASD分类系统显示出更高的准确性，灵敏度和特异性。对于繁忙的医疗诊所和诊断人员来说，这确实是非常有益的，并且可能是将来改变ASD诊断过程方式的新方向。特别是，与传统的筛选方法相比，拟议的基于CNN的ASD分类系统显示出更高的准确性，灵敏度和特异性。对于繁忙的医疗诊所和诊断人员而言，这确实是非常有益的，并且可能是将来改变ASD诊断过程方式的新方向。特别是，与传统的筛选方法相比，拟议的基于CNN的ASD分类系统显示出更高的准确性，灵敏度和特异性。对于繁忙的医疗诊所和诊断人员来说，这确实是非常有益的，并且可能是将来改变ASD诊断过程方式的新方向。