Krishnappa Babu Pradeep Raj, Di Martino J Matias, Aiello Rachel, Eichner Brian, Espinosa Steven, Green Jennifer, Howard Jill, Perochon Sam, Spanos Marina, Vermeer Saritha, Dawson Geraldine, Sapiro Guillermo
Department of Electrical and Computer Engineering, Duke University, Durham, NC.
Informatics and Computer Science, Catholic University of Uruguay, Montevideo, Uruguay.
NEJM AI. 2024 Oct;1(10). doi: 10.1056/AIcs2400510. Epub 2024 Sep 26.
Early detection of autism is important for timely access to diagnostic evaluation and early intervention services, which improve children's outcomes. Despite the ability of clinicians to reliably diagnose autism in toddlers, diagnosis is often delayed. SenseToKnow is a mobile autism screening application (app) delivered on a smartphone or tablet that provides an objective and quantitative assessment of early behavioral signs of autism based on computer vision (CV) and machine learning (ML). This study examined the accuracy of SenseToKnow for autism detection when the app was downloaded and administered remotely at home by caregivers using their own devices. The SenseToKnow app was administered by caregivers of 620 toddlers between 16 and 40 months of age, 188 of whom were subsequently diagnosed with autism by expert clinicians. The app displayed strategically designed movies and a bubble-popping game on an iPhone or iPad while recording the child's behavioral responses through the device's front-facing camera and touch/inertial sensors. Recordings of the child's behavior were then automatically analyzed using CV. Multiple behavioral phenotypes were quantified and combined using ML in an algorithm for autism prediction. SenseToKnow demonstrated a high level of diagnostic accuracy with area under the receiver operating characteristic curve of 0.92, sensitivity of 83.0%, specificity of 93.3%, positive predictive value of 84.3%, and negative predictive value of 92.6%. Accuracy of the app for detecting autism was similar when administered on either a caregiver's iPhone or iPad. These results demonstrate that a mobile autism screening app based on CV can be delivered remotely by caregivers at home on their own devices and can provide a high level of accuracy for autism detection. Remote screening for autism potentially lowers barriers to autism screening, which could reduce disparities in early access to services and support and improve children's outcomes.
早期发现自闭症对于及时获得诊断评估和早期干预服务非常重要，这些服务可改善儿童的预后。尽管临床医生有能力可靠地诊断幼儿自闭症，但诊断往往会延迟。SenseToKnow是一款可在智能手机或平板电脑上运行的移动自闭症筛查应用程序（app），它基于计算机视觉（CV）和机器学习（ML）对自闭症的早期行为迹象进行客观定量评估。本研究考察了在护理人员使用自己的设备在家中远程下载并使用SenseToKnow app进行自闭症检测时的准确性。620名16至40个月大幼儿的护理人员使用了SenseToKnow app，其中188名幼儿随后被专家临床医生诊断为自闭症。该应用程序在iPhone或iPad上播放精心设计的影片和泡泡龙游戏，同时通过设备的前置摄像头和触摸/惯性传感器记录孩子的行为反应。然后使用计算机视觉自动分析孩子行为的记录。多种行为表型通过机器学习进行量化，并结合在一个自闭症预测算法中。SenseToKnow表现出较高的诊断准确性，受试者工作特征曲线下面积为0.92，灵敏度为83.0%，特异性为93.3%，阳性预测值为84.3%，阴性预测值为92.6%。在护理人员的iPhone或iPad上使用该应用程序检测自闭症的准确性相似。这些结果表明，基于计算机视觉的移动自闭症筛查应用程序可以由护理人员在家中使用自己的设备远程提供，并且可以为自闭症检测提供较高的准确性。自闭症的远程筛查可能会降低自闭症筛查的障碍，这可以减少早期获得服务和支持方面的差距，并改善儿童的预后。
