Deep Learning to Transform Clinician Autism Diagnostic Assessments and More

NODA远程医疗系统通过以下方式改善了自闭症诊断评估的可及性： 指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，如果有必要， 诊断.该系统以证据为基础，已商业化， 发表研究来讨论这些益处。我们现建议改善这项服务， 在一个名为“NODA DL”的软件产品中开发深度（机器）学习功能 分类器“，以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NODA系统内将产生深远的影响，在时间达到一个坚定的 诊断，然后该能力可以随后用于有效地监测 自闭症患者的治疗进展。在这个项目中，我们将 确定DL在多大程度上改善了诊断过程。在第一阶段，我们将测试 使用以前生成的数据集来定性和量化潜在的效益。在第二阶段， 我们会进行一项临床研究，以记录节省时间和其他临床效益。 我们提出的NODA DL创新代表了识别和 然后是对自闭症患者的治疗，而不是渐进式的。这将导致一个重大的 改善健康结果和减少临床医生所需的时间， 心理学家办公室访问和分析视频数据。这种缩短的时间可以 转化为降低成本。我们预计，巨大的商业利益将 这一切都源于我们创新的计算机方法。

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.