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通过混合现实实现精确的医用小瓶识别:HoloLens 2的应用

Accurate Medical Vial Identification Through Mixed Reality: A HoloLens 2 Implementation.

作者信息

Mahmud Bahar Uddin, Hong Guan Yue, Sharmin Afsana, Asher Zachary D, Hoyle John D

机构信息

Department of Computer Science, Western Michigan University, Kalamazoo, MI 49008, USA.

Department of Mechanical and Aerospace Engineering, Western Michigan University, Kalamazoo, MI 49008, USA.

出版信息

Electronics (Basel). 2024 Nov 2;13(22):4420. doi: 10.3390/electronics13224420. Epub 2024 Nov 11.

DOI:10.3390/electronics13224420
PMID:40433188
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12110869/
Abstract

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20-25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach.

摘要

准确识别药瓶对于紧急医疗服务至关重要,尤其是对于那些外观相似但标签、容量和浓度不同的药瓶。本研究介绍了一种通过微软HoloLens 2利用混合现实技术实时检测药瓶的方法。该系统还配备了一个SQL服务器来管理条形码和药瓶信息。我们对HoloLens 2摄像头和外部扫描仪的条形码检测能力进行了对比分析。在正常光照条件下,当较大的条形码距离HoloLens 2为20 - 25厘米时,它能有效识别。然而,它在检测较小的条形码时面临困难,而外部扫描仪却能持续检测到这些小条形码。帧率调查显示了性能波动:在标准光照条件下平均每秒10.54帧(fps),在低光照下降至10.10 fps,当面对高条形码密度时进一步降至10.05 fps。分辨率测试表明,屏幕分辨率为1920×1080时准确率最高,精确率为98%。另一方面,分辨率为1280×720在准确率93%和速度之间取得了良好平衡。HoloLens 2在理想情况下表现令人满意;然而,需要改进检测算法和摄像头分辨率以适应不同环境。这种方法旨在帮助护理人员在危急情况下快速做出准确决策,并解决诸如依赖网络和人为错误等常见障碍。与仅使用扫描仪的方法相比,我们将外部蓝牙扫描仪与混合现实设备集成的新混合方法产生了最佳结果

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本文引用的文献

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Extended Reality Telemedicine Collaboration System Using Patient Avatar Based on 3D Body Pose Estimation.基于 3D 人体姿势估计的患者化身的扩展现实远程医疗协作系统。
Sensors (Basel). 2023 Dec 20;24(1):27. doi: 10.3390/s24010027.
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On-Device Execution of Deep Learning Models on HoloLens2 for Real-Time Augmented Reality Medical Applications.基于 HoloLens2 的深度学习模型在实时增强现实医疗应用中的设备端执行。
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Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis.
基于深度学习的三维容积图像分割与微观结构分析。
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CAL-Tutor: A HoloLens 2 Application for Training in Obstetric Sonography and User Motion Data Recording.CAL-Tutor:一款用于产科超声检查培训及用户运动数据记录的HoloLens 2应用程序。
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An Analysis of Prehospital Pediatric Medication Dosing Errors after Implementation of a State-Wide EMS Pediatric Drug Dosing Reference.实施全州范围内的 EMS 儿科药物剂量参考后,对院前儿科用药剂量错误的分析。
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Medication Errors in Pediatrics: Proposals to Improve the Quality and Safety of Care Through Clinical Risk Management.儿科用药错误:通过临床风险管理提高医疗质量和安全性的建议。
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Barcode medication administration technology use in hospital practice: a mixed-methods observational study of policy deviations.医院实践中条形码给药技术的应用:一项关于政策偏差的混合方法观察性研究
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