PFI-TT: Novel Artificial Intelligence Approach for Automatic Identification of Genetic and Neuroimaging Markers of Autism Spectrum Disorder

这一创新-技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是将被诊断患有自闭症谱系障碍（ASD）的患者的年龄降低到大约六个月，并详细了解个体福尔斯属于自闭症谱系。这些目标将通过一个支持人工智能（AI）的软件框架来实现，该框架将分析ASD风险儿童的大脑结构和其他医疗信息。该系统可以减轻获得自闭症诊断的负担，在目前的临床实践中，这需要大量的时间和数千美元。ASD症状到大脑区域的详细映射可以帮助儿科医生和其他专家更好地传达他们的诊断结果，并告知他们的治疗计划。此外，降低自闭症的诊断年龄将为父母和照顾者提供更大的时间窗口，以应用早期强化行为干预，这是已知的，以改善自闭症儿童的结果。拟议的项目旨在生产一个计算机辅助诊断（CAD）系统，用于自闭症诊断的基础上，从多模态脑成像和基因组风险因素的客观指标。儿童自闭症的诊断目前依赖于对儿童行为的主观评价。诊断过程可以开始早在一岁或两岁，但它继续通过3-4岁的随访观察，直到最终的自闭症诊断。 这个过程总共需要花费5000 - 7000美元。拟议的CAD系统预计将产生一个快速的诊断，目前的成本的一小部分。它旨在识别大脑解剖学的特定方面（来自结构磁共振成像[MRI]），以及与ASD的特定行为亚型相关的大脑连接性（来自功能和扩散MRI）。结合患者基因组中与ASD相关的变异来评估这些神经学数据，将产生ASD神经病学中涉及的大脑区域和神经回路图的详细概况。该地图将使用深度机器学习来完成，以在一个回顾性的高危婴儿队列中训练系统，这些婴儿在一岁之前接受了大脑成像，后来被诊断患有自闭症。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to reduce the age of patients diagnosed with autism spectrum disorders (ASD) to approximately six months and produce a detailed picture of where an individual falls on the autism spectrum. These aims will be accomplished via an Artificial Intelligence (AI)-enabled software framework that will analyze brain structure and other medical information from children at risk for ASD. The system may reduce the burden of obtaining a diagnosis of autism, which takes a significant amount of time and thousands of dollars under current clinical practice. The detailed mapping of ASD symptoms to brain regions may help pediatricians and other specialists better communicate their diagnostic findings and inform their plans for treatment. Furthermore, reduction in age at which autism can be diagnosed will give parents and caregivers a greater window of time to apply early intensive behavioral interventions which are known to improve outcomes in autistic children.The proposed project aims to produce a computer-assisted diagnostic (CAD) system for autism diagnosis based on objective metrics derived from multimodal brain imaging and genomic risk factors. Pediatric autism diagnosis currently relies on subjective evaluations of child behavior. A diagnostic process can begin as early as one or two years of age, but it continues with follow-up observations through age 3–4 years till a final autism diagnosis is rendered. This process can cost $5000–$7000 in total. The proposed CAD system is anticipated to produce a rapid diagnosis at a fraction of current cost. It seeks to identify specific facets of brain anatomy (from structural magnetic resonance imaging [MRI]), and brain connectivity (from functional and diffusion MRI) that will correlate with specific behavioral subtypes of ASD. Evaluating these neurological data in concert with ASD-related variations in the patient’s genome, will produce a detailed profile of brain regions and neural circuit maps implicated in ASD symptomatology. This map will be accomplished using deep machine learning to train the system on a retrospective cohort of high-risk infants, who underwent brain imaging prior to one year of age and were later diagnosed with autism. The system will be validated against an independent dataset.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.