Deep Learning to Transform Clinician Autism Diagnostic Assessments and More

野田远程健康系统通过以下方式改善自闭症诊断评估的可及性 引导家庭在家中分享他们孩子的视频剪辑，以便诊断临床医生可以 直接观察和标记任何非典型行为的视频，并在必要时呈现 诊断。这个系统是以证据为基础的，并已商业化，有几个 发表了讨论其益处的研究。我们现在建议通过以下方式改善这项服务 在名为‘Noda DL’的软件产品中开发深度(机器)学习能力 帮助临床医生更快地识别和更好地量化典型和非典型 他们从家人那里收到的视频上的行为。如果成功，此Noda DL功能 在野田系统内将产生深远的影响，在时间上达成一致 诊断，然后可以随后使用该功能来有效地监视 自闭症患者的治疗进展。在这个项目中，我们将 确定DL在多大程度上改进了诊断过程。在第一阶段，我们将测试我们的 使用以前生成的数据集来鉴定和量化潜在的好处。在第二阶段， 我们将进行一项临床研究，以证明节省时间和其他临床好处。 我们提议的野田DL创新代表着身份识别和 然后是对自闭症患者的护理，而不是增量护理。这将导致一场重大的 改善了健康结果，缩短了临床医生或 用于办公室访问和分析视频数据的心理学家。这种缩短的时间可以 转化为降低了成本。我们预计，重大的商业利益将 这是使用我们创新的计算机方法的结果。

NODA Telehealth system improves access to an autism diagnostic assessment by guiding families to share video clips of their child at home, so diagnostic clinicians can directly observe and ‘tag’ video of any atypical behavior, and if warranted, render a diagnosis. This system is evidence-based and has been commercialized, with several published studies to discuss the benefits. We now propose to improve this service by developing a Deep (machine) Learning capability in a software product called ‘NODA DL Classifier’ to help clinicians more quickly identify and better quantify typical and atypical behaviors on videos they receive from families. If successful, this NODA DL feature within the NODA system will have a profound impact in the time to reach a firm diagnosis, and then the capability could be used subsequently to effectively monitor treatment progress of individuals diagnosed with autism. In this project, we will determine how much DL improves the diagnostic process. In Phase I, we will test our use previously generated datasets to qualify and quantify potential benefits. In Phase II, we will conduct a clinical study to document time-savings and other clinical benefits. Our proposed NODA DL innovation represents a large step change in identification and then the care for ASD individuals, not an incremental one. It will lead to a significant improvement in both health outcomes and in reduced time required by clinicians or psychologists for office visits and for analyzing video data. This reduced time can be translated into reduced costs. We anticipate that significant commercial benefits will result from the use of our innovative computer methodologies.