@文章{信息:doi/10.2196/23130，作者=“Kim, Ho Heon and An, Jae Il and Park, Yu Rang”，标题=“通过数字生物标记驱动的深度学习在严肃游戏中检测学龄前儿童发育障碍的预测模型:发展研究”，期刊=“JMIR严肃游戏”，年=“2021”，月=“Jun”，日=“4”，卷=“9”，数=“2”，页=“e23130”，关键词=“发育迟缓;诊断预测;深度学习;严肃游戏;数字健康;数字表型出现;背景:儿童发育障碍的早期发现至关重要，因为早期干预可以改善儿童的预后。同时，越来越多的证据表明发育性残疾与运动技能之间存在关系，因此，运动技能被考虑在发育性残疾的早期诊断中。然而，在发展障碍的诊断中评估运动技能存在挑战，例如缺乏专家和时间限制，因此通常通过对父母的非正式问题或调查来进行。目的:本研究旨在评估使用拖放数据作为数字生物标志物的可能性，并建立基于拖放数据的分类模型，用于对发育障碍儿童进行分类。 Methods: We collected drag-and-drop data from children with typical development and developmental disabilities from May 1, 2018, to May 1, 2020, via a mobile application (DoBrain). We used touch coordinates and extracted kinetic variables from these coordinates. A deep learning algorithm was developed to predict potential development disabilities in children. For interpretability of the model results, we identified which coordinates contributed to the classification results by applying gradient-weighted class activation mapping. Results: Of the 370 children in the study, 223 had typical development, and 147 had developmental disabilities. In all games, the number of changes in the acceleration sign based on the direction of progress both in the x- and y-axes showed significant differences between the 2 groups (P<.001; effect size >0.5). The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities, with an area under the receiving operating characteristics curve of 0.817. A gradient class activation map, which can interpret the results of a deep learning model, was visualized with the game results for specific children. Conclusions: Through the results of the deep learning model, we confirmed that drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities. ", issn="2291-9279", doi="10.2196/23130", url="https://games.www.mybigtv.com/2021/2/e23130", url="https://doi.org/10.2196/23130", url="http://www.ncbi.nlm.nih.gov/pubmed/34085944" }