Department of Electrical and Computer Engineering, School of Engineering, Rutgers The State University of New Jersey, Piscataway, NJ, USA.
Department of Biomedical Engineering, School of Engineering, Rutgers The State University of New Jersey, Piscataway, NJ, USA.
Analyst. 2021 Apr 26;146(8):2531-2541. doi: 10.1039/d0an02451a.
Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R2 value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p-value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).
基于智能手机的便携式荧光显微镜由于其能够以常规台式显微镜成本的一小部分提供主要功能而变得越来越受欢迎。然而，基于智能手机的显微镜仍然仅限于单个荧光团、固定放大倍数、无法与不同的智能手机配合使用以及对玻璃载玻片或盖玻片的有限可用性。为了克服这些挑战，我们在这里提出了一种模块化的基于智能手机的显微镜附件。模块化设计允许用户轻松地在不同的滤光片和透镜组之间进行切换，从而实现了多个荧光团和放大倍数的实用性。我们的智能手机显微镜附件也可以与不同的智能手机一起使用，并在诺基亚 Lumia 1020、三星 Galaxy S9+ 和 iPhone XS 上进行了测试。此外，我们展示了在玻璃载玻片、盖玻片和微流控设备上的样品成像结果。使用 1951 年美国空军分辨率测试靶标来量化显微镜的最大分辨率，发现其最大分辨率为 3.9μm。我们将基于智能手机的显微镜的性能与台式显微镜进行了比较，并用从罗伯特伍德约翰逊医疗医院收集的人血样本中分离出的聚苯乙烯珠和血细胞得出了 R2 值为 0.99。此外，为了计算从基于智能手机的荧光显微镜成像的颗粒（细胞和珠子）的数量，我们使用多种训练算法开发了人工神经网络 (ANNs)，并评估了它们与对照 (ImageJ) 的性能。最后，我们进行了方差分析和 Tukey 的事后检验分析，发现 p 值为 0.97，这表明经过训练的 ANN 和对照 (ImageJ) 的性能之间没有统计学上的显著差异。
