PDF(Pubmed)
Abstract：
BACKGROUND: Adoption of 3D imaging systems in humanitarian settings requires accuracy comparable with manual measurement notwithstanding additional constraints associated with austere settings.
OBJECTIVE: This study aimed to evaluate the accuracy of child stature and mid-upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system (third generation) developed by Body Surface Translations Inc.
METHODS: A study of device accuracy was embedded within a 2-stage cluster survey at the Malakal Protection of Civilians site in South Sudan conducted between September 2021 and October 2021. All children aged 6 to 59 months within selected households were eligible. For each child, manual measurements were obtained by 2 anthropometrists following the protocol used in the 2006 World Health Organization Child Growth Standards study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. The scans were processed using a fully automated algorithm. 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.
OBJECTIVE: This study aimed to evaluate the accuracy of child stature and mid-upper arm circumference (MUAC) measurements produced by the AutoAnthro 3D imaging system (third generation) developed by Body Surface Translations Inc.
METHODS: A study of device accuracy was embedded within a 2-stage cluster survey at the Malakal Protection of Civilians site in South Sudan conducted between September 2021 and October 2021. All children aged 6 to 59 months within selected households were eligible. For each child, manual measurements were obtained by 2 anthropometrists following the protocol used in the 2006 World Health Organization Child Growth Standards study. Scans were then captured by a different enumerator using a Samsung Galaxy 8 phone loaded with a custom software, AutoAnthro, and an Intel RealSense 3D scanner. The scans were processed using a fully automated algorithm. 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.
摘要：
背景：在人道主义环境中采用3D成像系统需要与手动测量相当的精度，尽管存在与严格设置相关的额外限制。
目的：这项研究旨在评估由BodySurfaceTranslationsInc.开发的AutoAnthro3D成像系统（第三代）产生的儿童身高和上臂中围（MUAC）测量的准确性。
方法：在2021年9月至10月在南苏丹马拉卡勒平民保护站点进行的2阶段集群调查中，对设备准确性进行了研究。选定家庭中所有6至59个月的儿童都有资格。对于每个孩子，根据2006年世界卫生组织儿童生长标准研究中使用的方案,由2名人体肌层进行手动测量.然后，使用装有自定义软件的三星Galaxy8手机，由不同的枚举器捕获扫描结果，AutoAnthro,和英特尔实感3D扫描仪。使用全自动算法处理扫描。拟合多变量逻辑回归模型以评估实现成功扫描的调整几率。使用Bland-Altman图直观评估测量的准确性，并使用平均偏差进行量化，协议限制(LoAs)，以及个体差异的95%精度区间。主要的线人访谈是与调查列举员和BodySurfaceTranslationsInc开发人员进行的远程访谈，以了解beta测试中的挑战，培训,数据采集和传输。
结果：对539名符合条件的儿童进行了手动测量，并且扫描衍生的测量结果已成功处理了234例（43.4％）。至少10.4％（56/539）的儿童看护者拒绝同意扫描捕获；其他扫描未成功传输到服务器。儿童的人口统计学特征(年龄和性别)身材,MUAC也不与扫描衍生测量的可用性相关;团队显著相关(P<.001)。以cm为单位的扫描衍生测量的平均偏差对于身高为-0.5(95%CI-2.0至1.0),对于MUAC为0.7(95%CI0.4-1.0)。为了身材，95%LoA为-23.9cm至22.9cm。对于MUAC，95%LoA为-4.0cm至5.4cm。所有准确性指标因团队而异。与COVID-19大流行相关的物理距离和旅行政策限制了验证设备算法的测试，并阻止了开发人员进行亲自培训和现场监督，负面影响扫描捕获的质量，processing,和传输。
结论：扫描衍生的测量对于当前技术的广泛采用来说不够准确。尽管该软件显示出希望，需要对软件算法进行进一步的投资，以解决扫描传输和极端现场环境的问题，以及改进现场监督。团队准确性的差异提供了证据，表明对培训的投资也可以提高绩效。
目的：这项研究旨在评估由BodySurfaceTranslationsInc.开发的AutoAnthro3D成像系统（第三代）产生的儿童身高和上臂中围（MUAC）测量的准确性。
方法：在2021年9月至10月在南苏丹马拉卡勒平民保护站点进行的2阶段集群调查中，对设备准确性进行了研究。选定家庭中所有6至59个月的儿童都有资格。对于每个孩子，根据2006年世界卫生组织儿童生长标准研究中使用的方案,由2名人体肌层进行手动测量.然后，使用装有自定义软件的三星Galaxy8手机，由不同的枚举器捕获扫描结果，AutoAnthro,和英特尔实感3D扫描仪。使用全自动算法处理扫描。拟合多变量逻辑回归模型以评估实现成功扫描的调整几率。使用Bland-Altman图直观评估测量的准确性，并使用平均偏差进行量化，协议限制(LoAs)，以及个体差异的95%精度区间。主要的线人访谈是与调查列举员和BodySurfaceTranslationsInc开发人员进行的远程访谈，以了解beta测试中的挑战，培训,数据采集和传输。
结果：对539名符合条件的儿童进行了手动测量，并且扫描衍生的测量结果已成功处理了234例（43.4％）。至少10.4％（56/539）的儿童看护者拒绝同意扫描捕获；其他扫描未成功传输到服务器。儿童的人口统计学特征(年龄和性别)身材,MUAC也不与扫描衍生测量的可用性相关;团队显著相关(P<.001)。以cm为单位的扫描衍生测量的平均偏差对于身高为-0.5(95%CI-2.0至1.0),对于MUAC为0.7(95%CI0.4-1.0)。为了身材，95%LoA为-23.9cm至22.9cm。对于MUAC，95%LoA为-4.0cm至5.4cm。所有准确性指标因团队而异。与COVID-19大流行相关的物理距离和旅行政策限制了验证设备算法的测试，并阻止了开发人员进行亲自培训和现场监督，负面影响扫描捕获的质量，processing,和传输。
结论：扫描衍生的测量对于当前技术的广泛采用来说不够准确。尽管该软件显示出希望，需要对软件算法进行进一步的投资，以解决扫描传输和极端现场环境的问题，以及改进现场监督。团队准确性的差异提供了证据，表明对培训的投资也可以提高绩效。
