Chua Mei Chien, Hadimaja Matthew, Wong Jill, Mukherjee Sankha Subhra, Foussat Agathe, Chan Daniel, Nandal Umesh, Yap Fabian
Department of Neonatology, KK Women's and Children's Hospital, Singapore, Singapore.
Duke-NUS Medical School, Singapore, Singapore.
JMIR Pediatr Parent. 2024 Nov 22;7:e59564. doi: 10.2196/59564.
Length measurement in young children younger than 18 months is important for monitoring growth and development. Accurate length measurement requires proper equipment, standardized methods, and trained personnel. In addition, length measurement requires young children's cooperation, making it particularly challenging during infancy and toddlerhood.
This study aimed to develop a length artificial intelligence (LAI) algorithm to aid users in determining recumbent length conveniently from smartphone images and explore its performance and suitability for personal and clinical use.
This proof-of-concept study in healthy children (aged 0-18 months) was performed at KK Women's and Children's Hospital, Singapore, from November 2021 to March 2022. Smartphone images were taken by parents and investigators. Standardized length-board measurements were taken by trained investigators. Performance was evaluated by comparing the tool's image-based length estimations with length-board measurements (bias [mean error, mean difference between measured and predicted length]; absolute error [magnitude of error]). Prediction performance was evaluated on an individual-image basis and participant-averaged basis. User experience was collected through questionnaires.
A total of 215 participants (median age 4.4, IQR 1.9-9.7 months) were included. The tool produced a length prediction for 99.4% (2211/2224) of photos analyzed. The mean absolute error was 2.47 cm for individual image predictions and 1.77 cm for participant-averaged predictions. Investigators and parents reported no difficulties in capturing the required photos for most participants (182/215, 84.7% participants and 144/200, 72% participants, respectively).
The LAI algorithm is an accessible and novel way of estimating children's length from smartphone images without the need for specialized equipment or trained personnel. The LAI algorithm's current performance and ease of use suggest its potential for use by parents or caregivers with an accuracy approaching what is typically achieved in general clinics or community health settings. The results show that the algorithm is acceptable for use in a personal setting, serving as a proof of concept for use in clinical settings.
ClinicalTrials.gov NCT05079776; https://clinicaltrials.gov/ct2/show/NCT05079776.
对18个月以下幼儿进行身长测量对于监测其生长发育至关重要。准确的身长测量需要合适的设备、标准化的方法以及经过培训的人员。此外，身长测量需要幼儿的配合，这使得在婴儿期和幼儿期进行测量尤其具有挑战性。
本研究旨在开发一种身长人工智能（LAI）算法，以帮助用户从智能手机图像中方便地确定卧位身长，并探索其性能以及在个人和临床应用中的适用性。
这项针对健康儿童（0至18个月）的概念验证研究于2021年11月至2022年3月在新加坡KK妇女儿童医院进行。智能手机图像由家长和研究人员拍摄。经过培训的研究人员进行标准化的身长板测量。通过将该工具基于图像的身长估计值与身长板测量值进行比较（偏差[平均误差，测量长度与预测长度之间的平均差异]；绝对误差[误差幅度]）来评估性能。在个体图像基础和参与者平均基础上评估预测性能。通过问卷调查收集用户体验。
共纳入215名参与者（中位年龄4.4岁，四分位距1.9 - 9.7个月）。该工具对所分析照片中的99.4%（2211/2224）给出了身长预测。个体图像预测的平均绝对误差为2.47厘米，参与者平均预测的平均绝对误差为1.77厘米。研究人员和家长报告称，对于大多数参与者（分别为182/215，84.7%的参与者和144/200，72%的参与者）来说，拍摄所需照片没有困难。
LAI算法是一种无需专门设备或经过培训的人员即可从智能手机图像中估计儿童身长的便捷且新颖的方法。LAI算法目前的性能和易用性表明，家长或照顾者使用它具有潜力，其准确性接近一般诊所或社区卫生机构通常能达到的水平。结果表明该算法在个人环境中使用是可接受的，为在临床环境中的应用提供了概念验证。
ClinicalTrials.gov NCT05079776；https://clinicaltrials.gov/ct2/show/NCT05079776
