Smartphone-based sensors in health and wellness monitoring–Perspectives and assessment of the emerging future

Pramanik, Himadri Sikhar; Pal, Arpan; Kirtania, Manish; Chakravarty, Tapas; Ghose, Avik

doi:10.1016/b978-0-12-823696-3.00018-0

Cited by 3 publications

(1 citation statement)

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“…According to Statista, there are currently over 6.8 billion smartphone users worldwide. Unsurprisingly, smartphones are increasingly being used in a range of wellness and healthcare applications, such as the diagnosis of skin lesions, , the quantification of lateral flow assays, − glucose detection, biomarker detection, and general health and activity monitoring . Additionally, because of the recent advances in both hardware and software capabilities, smartphones are now being used as digital microscopes for the analysis of bodily fluids.…”

Section: Introductionmentioning

confidence: 99%

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis

Aslan,

Ding,

Stavrakis

et al. 2023

Anal. Chem.

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We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/ particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

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