Song Kefan, Adams Alexander T
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States.
Front Digit Health. 2024 Nov 28;6:1461559. doi: 10.3389/fdgth.2024.1461559. eCollection 2024.
Current preoperative exam guidelines utilize extensive lab tests, including blood tests and urine analysis, which are crucial for assessing surgical readiness. However, logistical challenges, especially for patients traveling long distances for high-quality medical care, create significant delays and burdens. This study aims to address these challenges by applying a previously developed point-of-care (POC) device system to perform accurate and rapid lab tests. This device is designed to assist both healthcare providers in resource-limited settings and patients by offering a low-cost, portable diagnostic tool that enables both in-clinic and at-home testing.
The system was tested for adaptability and compatibility by transitioning from its original Android platform to an iOS platform. A custom application was developed to maintain the system's capabilities of capturing optimal cell images across different mobile platforms. The system's cell counting algorithm was tailored to process the captured images, featuring a streamlined workflow that includes image processing and automated cell detection using a Hough circle algorithm.
The new system provided good-quality raw images with 26.3 px/ m pixel resolution and 2.19 m spatial resolution, facilitating effective cell recognition and counting. The cell counting algorithm demonstrated high precision (0.8663) and high recall (0.9312), with a correlation ( ) between algorithm-generated counts and actual counts.
This study highlights the potential of the POC device to streamline preoperative testing, making it more accessible and efficient, particularly for patients in rural areas or those needing to travel for medical care. Future enhancements, including wider field-of-view, adjustable magnification, more advanced and integrated algorithms as well as integration with a microfluidic channel for direct sample analysis, are proposed to expand the device's functionality. The device's portability, ease of use, and rapid processing time position it as a promising alternative to traditional lab tests, ultimately aiming to improve patient care and surgical outcomes.
当前的术前检查指南采用了广泛的实验室检查,包括血液检查和尿液分析,这些对于评估手术准备情况至关重要。然而,后勤方面的挑战,尤其是对于长途跋涉寻求高质量医疗服务的患者来说,会造成严重的延误和负担。本研究旨在通过应用先前开发的即时检验(POC)设备系统来进行准确、快速的实验室检查,以应对这些挑战。该设备旨在通过提供一种低成本、便携式的诊断工具,帮助资源有限环境中的医疗服务提供者和患者进行诊所内和家庭检测。
通过从其原始的安卓平台过渡到iOS平台,对该系统的适应性和兼容性进行了测试。开发了一个定制应用程序,以保持系统在不同移动平台上捕获最佳细胞图像的能力。该系统的细胞计数算法经过定制,以处理捕获的图像,其简化的工作流程包括图像处理和使用霍夫圆算法进行自动细胞检测。
新系统提供了分辨率为26.3 px/μm像素和2.19 μm空间分辨率的高质量原始图像,便于有效的细胞识别和计数。细胞计数算法显示出高精度(0.8663)和高召回率(0.931),算法生成的计数与实际计数之间具有相关性( )。
本研究突出了POC设备简化术前检测的潜力,使其更易于获得且更高效,特别是对于农村地区的患者或那些需要长途就医治疗的患者。建议未来进行改进,包括更宽的视野、可调节的放大倍数、更先进和集成的算法,以及与微流控通道集成以进行直接样本分析,以扩展该设备的功能。该设备的便携性、易用性和快速处理时间使其成为传统实验室检测的一个有前途的替代方案,最终目标是改善患者护理和手术结果。