Early detection of autism using digital behavioral phenotyping

Sam Perochon

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17–36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.

自闭症是一种与社交沟通障碍有关的神经发育疾病，早期发现自闭症可确保及时干预。与研究相比，自闭症筛查问卷在基层医疗机构等实际环境中使用时的准确性较低，尤其是对有色人种儿童和女童而言。在此，我们报告了一项多临床、前瞻性研究的结果，该研究评估了自闭症筛查数字应用程序（App）的准确性，该应用程序是在儿科健康检查期间对 475 名（17-36 个月大）儿童（269 名男孩和 206 名女孩）进行的，其中 49 名儿童被诊断为自闭症，98 名儿童被诊断为发育迟缓，但没有自闭症。该应用程序显示的刺激物可诱发自闭症的行为表现，并通过计算机视觉和机器学习进行量化。结合多种数字表型的算法显示出很高的诊断准确性，接收者工作特征曲线下面积 = 0.90，灵敏度 = 87.8%，特异性 = 80.8%，阴性预测值 = 97.8%，阳性预测值 = 40.6%。该算法在按性别、种族和民族划分的亚组中具有相似的灵敏度表现。这些结果表明，数字表型技术有可能为现实环境中的自闭症筛查提供一种客观、可扩展的方法。此外，将数字表型和照顾者问卷调查的结果结合起来可能会提高自闭症筛查的准确性，并有助于减少诊断和干预方面的差异。