幼儿自闭症远程筛查移动应用程序的验证

NEJM AI Pub Date : 2024-10-01 Epub Date: 2024-09-26 DOI:10.1056/AIcs2400510
Pradeep Raj Krishnappa Babu, J Matias Di Martino, Rachel Aiello, Brian Eichner, Steven Espinosa, Jennifer Green, Jill Howard, Sam Perochon, Marina Spanos, Saritha Vermeer, Geraldine Dawson, Guillermo Sapiro
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引用次数: 0

摘要

自闭症的早期发现对于及时获得诊断评估和早期干预服务非常重要,从而改善儿童的预后。尽管临床医生有能力可靠地诊断幼儿的自闭症,但诊断往往被推迟。SenseToKnow是一款基于计算机视觉(CV)和机器学习(ML),对自闭症早期行为体征进行客观定量评估的智能手机或平板电脑上的移动自闭症筛查应用程序(app)。这项研究检查了SenseToKnow在自闭症检测方面的准确性,当护理人员下载应用程序并在家中使用自己的设备远程管理时。SenseToKnow应用程序由620名16至40个月大的幼儿的看护人管理,其中188名随后被临床专家诊断为自闭症。该应用程序在iPhone或iPad上播放精心设计的电影和泡泡游戏,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CV自动分析儿童行为的记录。在自闭症预测算法中使用ML对多种行为表型进行量化和组合。SenseToKnow的诊断准确率较高,患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%。在看护人的iPhone或iPad上使用这款检测自闭症的应用程序时,其准确性是相似的。这些结果表明,基于CV的移动自闭症筛查应用程序可以由护理人员在家中通过自己的设备远程交付,并且可以为自闭症检测提供高水平的准确性。远程自闭症筛查可能会降低自闭症筛查的障碍,从而减少早期获得服务和支持方面的差距,并改善儿童的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation of a Mobile App for Remote Autism Screening in Toddlers.

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.

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