TY - JOUR AU - Leidman, Eva AU - Jatoi, Muhammad Ali AU - Bollemeijer, Iris AU - Majer, Jennifer AU - Doocy, Shannon PY - 2022 DA - 2022/10/21 TI -低资源环境下儿童人体测量全自动3D成像系统的准确性:以致数十名马拉卡勒,效能评估南苏丹乔- JMIR生物医学Eng SP - e40066六世- 7 - 2 KW -移动健康KW - mHealth KW -儿童营养KW -人体测量学KW - 3 d成像KW -成像千瓦准确性KW -测量KW -孩子身材KW -软件KW -算法KW -自动化KW -设备KW -儿童健康KW -儿科健康KW -高度KW -长度KW -手臂周长AB -背景:在人道主义环境中采用3D成像系统需要与人工测量相媲美的精度，尽管与艰苦环境相关的其他限制。目的:本研究旨在评估体表翻译公司开发的第三代AutoAnthro 3D成像系统测量儿童身高和中上臂围(MUAC)的准确性。方法:2021年9月至2021年10月在南苏丹马拉卡尔平民保护地点进行的两阶段聚类调查中嵌入了设备精度研究。所有选定家庭中6至59个月的儿童都有资格参加。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工测量。然后，扫描结果由另一个枚举器捕获，该枚举器使用装有定制软件AutoAnthro的三星Galaxy 8手机和英特尔RealSense 3D扫描仪。扫描是用全自动算法处理的。多元逻辑回归模型适合评估调整后的成功扫描的几率。 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. SN - 2561-3278 UR - https://biomedeng.www.mybigtv.com/2022/2/e40066 UR - https://doi.org/10.2196/40066 DO - 10.2196/40066 ID - info:doi/10.2196/40066 ER -