Wells Mike, Goldstein Lara Nicole, Wells Terran, Ghazi Niloufar, Pandya Abhijit, Furht Borifoje, Engstrom Gabriella, Jan Muhammad Tanveer, Shih Richard
Department of Emergency Medicine Schmidt College of Medicine Florida Atlantic University Boca Raton Florida USA.
Department of Biomedical Engineering Florida International University Miami Florida USA.
J Am Coll Emerg Physicians Open. 2024 Oct 4;5(5):e13320. doi: 10.1002/emp2.13320. eCollection 2024 Oct.
Weight estimation is required in adult patients when weight-based medication must be administered during emergency care, as measuring weight is often not possible. Inaccurate estimations may lead to inaccurate drug dosing, which may cause patient harm. High-tech 3D camera systems driven by artificial intelligence might be the solution to this problem. The aim of this review was to describe and evaluate the published literature on 3D camera weight estimation methods.
A systematic literature search was performed for articles that studied the use of 3D camera systems for weight estimation in adults. Data on the study characteristics, the quality of the studies, the 3D camera methods evaluated, and the accuracy of the systems were extracted and evaluated.
A total of 14 studies were included, published from 2012 to 2024. Most studies used Microsoft Kinect cameras, with various analytical approaches to weight estimation. The 3D camera systems often achieved a P10 of 90% (90% of estimates within 10% of actual weight), with all systems exceeding a P10 of 78%. The studies highlighted a significant potential for 3D camera systems to be suitable for use in emergency care.
The 3D camera systems offer a promising method for weight estimation in emergency settings, potentially improving drug dosing accuracy and patient safety. Weight estimates were satisfactorily accurate, often exceeding the reported accuracy of existing weight estimation methods. Importantly, 3D camera systems possess characteristics that could make them very appropriate for use during emergency care. Future research should focus on developing and validating this methodology in larger studies with true external and clinical validation.
在成人患者的紧急护理中，当必须根据体重给药而又往往无法测量体重时，就需要进行体重估算。估算不准确可能导致药物剂量不准确，进而可能对患者造成伤害。由人工智能驱动的高科技3D摄像系统可能是解决这一问题的办法。本综述的目的是描述和评估已发表的关于3D摄像体重估算方法的文献。
对研究使用3D摄像系统估算成人体重的文章进行了系统的文献检索。提取并评估了有关研究特征、研究质量、所评估的3D摄像方法以及系统准确性的数据。
共纳入14项研究，发表时间为2012年至2024年。大多数研究使用微软Kinect摄像头，并采用了各种体重估算分析方法。3D摄像系统的P10（90%的估算值在实际体重的10%以内）通常能达到90%，所有系统的P10均超过78%。这些研究凸显了3D摄像系统在紧急护理中使用的巨大潜力。
3D摄像系统为紧急情况下的体重估算提供了一种有前景的方法，有可能提高药物剂量的准确性和患者安全性。体重估算的准确性令人满意，常常超过现有体重估算方法所报告的准确性。重要的是，3D摄像系统具有的特性使其非常适合在紧急护理中使用。未来的研究应侧重于在更大规模的研究中开发和验证这种方法，并进行真正的外部和临床验证。
