Wu Sixuan, Song Kefan, Cobb Jason, Adams Alexander T
School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
JMIR Biomed Eng. 2024 Dec 23;9:e62770. doi: 10.2196/62770.
Cell concentration in body fluid is an important factor for clinical diagnosis. The traditional method involves clinicians manually counting cells under microscopes, which is labor-intensive. Automated cell concentration estimation can be achieved using flow cytometers; however, their high cost limits accessibility. Microfluidic systems, although cheaper than flow cytometers, still require high-speed cameras and syringe pumps to drive the flow and ensure video quality. In this paper, we present SmartFlow, a low-cost solution for cell concentration estimation using smartphone-based computer vision on 3D-printed, pump-free microfluidic platforms.
The objective was to design and fabricate microfluidic chips, coupled with clinical utilities, for cell counting and concentration analysis. We answered the following research questions (RQs): RQ1, Can gravity drive the flow within the microfluidic chips, eliminating the need for external pumps? RQ2, How does the microfluidic chip design impact video quality for cell analysis? RQ3, Can smartphone-captured videos be used to estimate cell count and concentration in microfluidic chips?
To answer the 3 RQs, 2 experiments were conducted. In the cell flow velocity experiment, diluted sheep blood flowed through the microfluidic chips with and without a bottleneck design to answer RQ1 and RQ2, respectively. In the cell concentration analysis experiment, sheep blood diluted into 13 concentrations flowed through the microfluidic chips while videos were recorded by smartphones for the concentration measurement.
In the cell flow velocity experiment, we designed and fabricated 2 versions of microfluidic chips. The ANOVA test (Straight: F=6144.45, P<.001; Bottleneck: F=3475.78, P<.001) showed the height difference had a significant impact on the cell velocity, which implied gravity could drive the flow. The video sharpness analysis demonstrated that video quality followed an exponential decay with increasing height differences (video quality=100e) and a bottleneck design could effectively preserve video quality (Straight: R=0.95, k=4.33; Bottleneck: R=0.91, k=0.59). Samples from the 13 cell concentrations were used for cell counting and cell concentration estimation analysis. The accuracy of cell counting (n=35, 60-second samples, R=0.96, mean absolute error=1.10, mean squared error=2.24, root mean squared error=1.50) and cell concentration regression (n=39, 150-second samples, R=0.99, mean absolute error=0.24, mean squared error=0.11, root mean squared error=0.33 on a logarithmic scale, mean average percentage error=0.25) were evaluated using 5-fold cross-validation by comparing the algorithmic estimation to ground truth.
In conclusion, we demonstrated the importance of the flow velocity in a microfluidic system, and we proposed SmartFlow, a low-cost system for computer vision-based cellular analysis. The proposed system could count the cells and estimate cell concentrations in the samples.
体液中的细胞浓度是临床诊断的一个重要因素。传统方法是临床医生在显微镜下手动计数细胞，这一过程 labor-intensive。使用流式细胞仪可以实现细胞浓度的自动估计；然而，其高昂的成本限制了其普及。微流控系统虽然比流式细胞仪便宜，但仍需要高速相机和注射泵来驱动流体并确保视频质量。在本文中，我们展示了 SmartFlow，这是一种基于智能手机计算机视觉的低成本解决方案，用于在 3D 打印的无泵微流控平台上估计细胞浓度。
目的是设计并制造与临床应用相结合的微流控芯片，用于细胞计数和浓度分析。我们回答了以下研究问题（RQs）：问题 1，重力能否驱动微流控芯片内的流体流动，从而无需外部泵？问题 2，微流控芯片设计如何影响细胞分析的视频质量？问题 3，智能手机拍摄的视频能否用于估计微流控芯片中的细胞计数和浓度？
为回答这 3 个问题，进行了 2 项实验。在细胞流速实验中，稀释的羊血分别流过有无瓶颈设计的微流控芯片，以分别回答问题 1 和问题 2。在细胞浓度分析实验中，稀释成 13 种浓度的羊血流过微流控芯片，同时智能手机记录视频以进行浓度测量。
在细胞流速实验中，我们设计并制造了 2 个版本的微流控芯片。方差分析测试（无瓶颈：F = 6144.45，P <.001；有瓶颈：F = 3475.78，P <.001）表明高度差对细胞流速有显著影响，这意味着重力可以驱动流体流动。视频清晰度分析表明，视频质量随高度差增加呈指数衰减（视频质量 = 100e），并且瓶颈设计可以有效保持视频质量（无瓶颈：R = 0.95，k = 4.33；有瓶颈：R = 0.91，k = 0.59）。来自 13 种细胞浓度的样本用于细胞计数和细胞浓度估计分析。通过将算法估计与真实值进行比较，使用 5 折交叉验证评估细胞计数的准确性（n = 35，60 秒样本，R = 0.96，平均绝对误差 = 1.10，均方误差 = 2.24，均方根误差 = 1.50）和细胞浓度回归（n = 39，150 秒样本，R = 0.99，平均绝对误差 = 0.24，均方误差 = 0.11，对数尺度下的均方根误差 = 0.33，平均平均百分比误差 = 0.25）。
总之，我们证明了微流控系统中流速的重要性，并提出了 SmartFlow，这是一种用于基于计算机视觉的细胞分析的低成本系统。所提出的系统可以对样本中的细胞进行计数并估计细胞浓度。
