低资源环境下全自动儿童人体测量3D成像系统的准确性卡塔尔世界杯8强波胆分析南苏丹马拉卡尔的有效性评估A Leidman,Eva A Jatoi,Muhammad Ali, A Bollemeijer,Iris %A Majer,Jennifer %A Doocy,Shannon %+约翰霍普金斯大学彭博公共卫生学院国际卫生学系，马里兰州巴尔的摩市N. Wolfe街615号，21205，美国，144049085125，eleidman@jhu.edu %K移动健康%K移动健康%K儿童营养%K人体测量%K三维成像%K成像%K精度%K测量%K儿童身材%K软件%K算法%K自动化%K设备%K儿童健康%K儿童健康%K身高%K长度%K臂围%D 2022 %7 21.10.2022 %9原始论文%J JMIR生物医学工程%G英语%X在人道主义环境中采用3D成像系统需要与人工测量相当的精度，尽管与严峻环境相关的附加限制。目的:本研究旨在评估由Body Surface Translations公司开发的autoanthroo 3D成像系统(第三代)测量儿童身高和中上臂围(MUAC)的准确性。方法:在2021年9月至2021年10月期间，在南苏丹马拉卡勒平民保护地点进行了两阶段的集群调查，其中嵌入了一项装置准确性研究。选定家庭中所有6至59个月的儿童都符合条件。对于每个儿童，由2名人体测量学家按照2006年世界卫生组织儿童生长标准研究中使用的方案进行手动测量。扫描结果随后由另一名枚举员使用装有定制软件AutoAnthro的三星Galaxy 8手机和英特尔RealSense 3D扫描仪捕获。扫描是用全自动算法处理的。 A multivariate logistic regression model was fit to evaluate the adjusted odds of achieving a successful scan. The accuracy of the measurements was visually assessed using Bland-Altman plots and quantified using average bias, limits of agreement (LoAs), and the 95% precision interval for individual differences. Key informant interviews were conducted remotely with survey enumerators and Body Surface Translations Inc developers to understand challenges in beta testing, training, data acquisition and transmission. Results: Manual measurements were obtained for 539 eligible children, and scan-derived measurements were successfully processed for 234 (43.4%) of them. Caregivers of at least 10.4% (56/539) of the children refused consent for scan capture; additional scans were unsuccessfully transmitted to the server. Neither the demographic characteristics of the children (age and sex), stature, nor MUAC were associated with availability of scan-derived measurements; team was significantly associated (P<.001). The average bias of scan-derived measurements in cm was −0.5 (95% CI −2.0 to 1.0) for stature and 0.7 (95% CI 0.4-1.0) for MUAC. For stature, the 95% LoA was −23.9 cm to 22.9 cm. For MUAC, the 95% LoA was −4.0 cm to 5.4 cm. All accuracy metrics varied considerably by team. The COVID-19 pandemic–related physical distancing and travel policies limited testing to validate the device algorithm and prevented developers from conducting in-person training and field oversight, negatively affecting the quality of scan capture, processing, and transmission. Conclusions: Scan-derived measurements were not sufficiently accurate for the widespread adoption of the current technology. Although the software shows promise, further investments in the software algorithms are needed to address issues with scan transmission and extreme field contexts as well as to enable improved field supervision. Differences in accuracy by team provide evidence that investment in training may also improve performance. %R 10.2196/40066 %U https://biomedeng.www.mybigtv.com/2022/2/e40066 %U https://doi.org/10.2196/40066