Microfluidic Hall-effect biosensors detect superparamagnetic bead labels as they flow past a thin-film Hall element in a microchannel. Designing one couples bead magnetization, stray-field distribution, Hall transport, and channel flow across 14 parameters that finite-element solvers explore only at minutes to hours per configuration. We present a coupled analytical–numerical framework for this signal chain: Clausius–Mossotti bead magnetization with a volume fraction correction, a point-dipole stray field, a volume-averaged Hall voltage, Poiseuille transport, and a Johnson–Nyquist and Hooge 1/f noise model, evaluated across 12 sensor presets compiled from the literature, spanning graphene, III–V semiconductors, Si CMOS, bismuth, and topological insulators; any other platform can be defined from user-supplied transport parameters. Benchmarked against a companion COMSOL Multiphysics 6.0 study, the framework reproduces the Hall voltage to within 4.8% at a favorable bead-to-sensor area ratio and deviates by 22% and 15% at off-optimum geometries, consistent with the point-dipole near-field limit at h/rb=1. Three design rules follow: a signal-to-noise ridge at sensor widths comparable to the bead diameter (w*≈db; area ratios 0.4–1.0 at constant voltage, 0.5–2.6 at constant current), matching reported single-bead geometries; a material choice that must be made under an explicit electrical drive constraint; and a sampling-limited flow-velocity window. Predicted signals agree at the order-of-magnitude level with published InAs and Si CMOS experiments. We release the model as a freely accessible, no-install browser implementation with a built-in 2D axisymmetric magnetostatic finite-element (FEM) solver that maps where the dipole approximation degrades.

The need to diagnose complex and novel diseases has introduced significant advances in biosensing technologies, more specifically, miniaturized bio-electronics methods. Many of these diagnostic systems revolve around bioparticle quantification based on electrochemical impedance spectrometry (EIS). However, the robustness and accuracy of these systems still need improvement because of the nature of the electrical signals generated by the bioparticles, which are subject to a multitude of complex noise sources in microelectronics systems. Therefore, it is important to understand the behavior of different noiseintroducing elements in the desired frequency bandwidth to better design the output signal detection and filtering circuits. Here, we modeled the microelectronics impedance spectrometry system and performed the noise simulations of two configurations of the EIS system. It was observed that for a microfluidic channel with an estimated resistance of 100k Omega, the feedback resistors introduce a noise of RMS 3mV in a dual-electrode configuration and a noise of RMS 7mV in a three-electrodes (differential) configuration in the bandwidth of 1THz. This information can be employed in designing effective noise-filtering circuits and selecting the operable bandwidth of the EIS system, as the system introduces noise in the order of a few hundred microvolts in the 3- 10 MHz frequency range.

Numerically modeling the magnetic field perturbations induced by micrometer-scale magnetized particles in thin-film Hall sensors finds applications in various detection modalities. Here, we present the classical Drude model of galvanomagnetic transport to describe the Hall effect and implement it within COMSOL Multiphysics Software to simulate how magnetized microparticles influence the signal in various semiconductor thin films, including Silicon, Indium Antimonide(InSb), Indium Arsenide (InAs), Gallium Arsenide(GaAs), Graphene, Mercury Cadmium Telluride(HgCdTe) and Cadmium Manganese Telluride(CdMnTe). By applying simplified forms of the full Hall effect equations, we relate material properties-such as electrical conductivity and Hall coefficients-to the sensor's sensitivity to localized stray fields. To efficiently evaluate magnetostatic potentials and fields, we employ a combined FEM-BEM (Finite Element Method-Boundary Element Method) approach, thus avoiding additional auxiliary domains. Our results provide a robust framework for designing high-sensitivity Hall sensors, guiding future experimental realizations, and enabling scalable solutions for next-generation magnetic sensing technologies.

Fluorescence microscopy enabled by smartphone-coupled 3D instruments has shown utility in different biomedical applications ranging from diagnostics to biomanufacturing. Recently, we have designed and developed these devices and have demonstrated their utility in micro-nano particle sensing and leukocyte imaging. Here, we present a novel application for enhancing the imaging performance of smartphone fluorescence microscopes (SFM) and reducing their operational complexity. Computational noise correction is employed using 3D Averaging and 3D Gaussian filters of different kernel sizes (3 × 3 × 3, 7 × 7 × 7, 11 × 11 × 11, 15 × 15 × 15, and 21 × 21 × 21) and various standard deviations σ (for Gaussian only). Fluorescent beads of different sizes (8.3, 2, 1, 0.8 µm) were imaged using a custom-designed SFM. The application of the computational filters significantly enhanced the signal quality of particle detection in the captured fluorescent images. Amongst the Averaging filters, a kernel size of 21 × 21 × 21 produced the best results for all bead sizes, and similarly, amongst Gaussian filters, σ equal to 5 and a kernel size equal to 21 × 21 × 21 produced the best results. This visual improvement was then quantified by calculating the signal-difference-to-noise ratio (SDNR) and contrast-to-noise ratio (CNR) of filtered and unfiltered original images using a custom-developed quality assessment algorithm (AQAFI). Lastly, noise correction using Averaging and Gaussian filters with the previously identified optimal parameters was applied to images of fluorescently tagged human peripheral blood leukocytes captured using an SFM under various conditions. The ubiquitous nature and simplistic application of these filters enable their utility with a range of existing fluorescence microscope designs, thus allowing us to enhance their imaging capabilities.

Portable fluorescence microscopes coupled with smartphones offer accessible and cost-effective point-of-care diagnostic solutions, but often produce noisy and blurry images with poor contrast. Here, we introduce HIST-DIP (HIStogram Thresholding and Deep Image Prior), an unsupervised framework for fluorescence microscopy image restoration. Histogram thresholding isolates fluorescence signals by removing background noise, while DIP refines structural details and enhances resolution without large labeled datasets. Validation results show substantial quality gains including the average Peak Signal-to-Noise Ratio (PSNR) improved from 15.59 dB to 27.10 dB, and the Structural Similarity Index Measure (SSIM) rose from 0.035 to 0.82. Contrast-to-noise ratio (CNR) and signal difference-to-noise ratio (SDNR) also increased significantly, indicating sharper bead outlines and reduced background interference. Unlike conventional deep learning methods, HIST-DIP needs no external training data, making it well-suited for real-time, low-cost, and point-of-care diagnostic imaging. These findings highlight the potential of HIST-DIP in enhancing the quality of smartphone-based microscopy images, while also motivating future research towards optimizing the methods for real-time on-device computations.

Cellular surface receptors are commonly used as diagnostic and prognostic biomarkers for many infectious diseases. Benchtop clinical instruments, e.g., flow cytometers and fluorescence microscopes are commonly employed for identification and quantification of these biomarkers. These diagnostic techniques suffer from high instrument cost, laborious protocols and limited multiplexing ability. Recently, we have demonstrated the surface receptor detection of blood cells using novel electrically sensitive microparticles. Here, we employ computational pruning and machine learning techniques to improve the detection of cell surface receptors as biomarkers. The data collected from microfluidic impedance flow cytometry consisting of a multifrequency response of metal oxide-coated microparticles conjugated to blood cells (granulocytes) with either CD11b or CD66b surface receptors and video recordings were transformed to numerical values and manually annotated to train machine learning models. Classification accuracies of ∼95% and 97% before and after applying outlier removal techniques were observed when differentiating between cells and cells with 10 nm-Al2O3 anti-CD11b conjugated particles. Classification accuracies of ∼94% and ∼96% were observed when differentiating between cells and cells with 20 nm-Al2O3 anti-CD66b conjugated particles. Similarly, an improvement in the classification of neural networks was observed for different applications of the impedance spectrometry platform data. This provides preliminary results that impedance cytometer data collected for multiple biomarkers, along with the trained machine-learning models and noise reduction techniques, can efficiently quantify the biomarkers. This study will help in enabling next-generation cytometry technology with a potential for improved disease diagnosis in the future.

Phagocytosis is a critical component of innate immunity that helps the body defend itself against infection, foreign particles, and cellular debris. Investigating and quantifying phagocytosis can help understand how the immune system identifies foreign particles and how phagocytosis relates to other biomarkers, e.g., cytokines, cell surface receptors, or blood lactate levels. In particular, increased blood lactate levels can be a potential biomarker to study diseases, e.g., septic shock. Establishing a relationship between phagocytosis and lactate levels can serve as an effective tool to monitor the immune response and may help stratify patients. In this study, we use phagocytosis activity data to classify the patients into two groups of blood lactate levels (High and Low) with machine learning models. The neutrophils extracted from the whole blood samples of 19 patients were used to collect data on phagocytosis, where the neutrophils were allowed to internalize IgG coated fluorescent bioparticles. The data collection process involved collecting whole blood samples, neutrophil isolation, adding fluorescent beads, incubating, and imaging the sample using a fluorescence microscope. The phagocytosis assay images were used to generate a numerical dataset by manually counting the number of particles engulfed by each cell. The study first presents an improved understanding by employing hierarchical clustering and heatmaps to generate the graphical representation of phagocytosis data. By comparing the results of heat maps and clustering techniques, it can be observed that the phagocytosis activity data can be used to differentiate blood lactate levels in two groups (control and high-risk). Later, three machine learning models (Decision Tree, k-nearest Neighbor, and Naïve Bayes) were trained on the original and pruned datasets after the outliers were removed. The AI models classified the data into high-risk and low-risk groups of blood lactate levels. A maximum classification accuracy of 78% and an area under the curve of 0.78 was achieved using the trained models.

In biomedical applications, the precise control and sensing of magnetic micro/ nanoparticles play a critical role in targeted drug delivery and diagnostics. The functionality and sensing of these particles depend largely on the characteristics of the external magnetic field, making it a vital component of associated biomedical platform designs. This study introduces an optimized Halbach array design that creates a uniform magnetic field, enhancing the precision of magnetizing paramagnetic particle for biomedical applications. Here, we leverage this innovation to design a Hall Effect sensor to detect magnetically influenced particles passing over the sensor surface, while subsequently registering distinct Hall voltage peaks. Our results demonstrate the enhanced efficacy of paramagnetic particle magnetization compared to non-magnetized particles, supporting this design strategy. This sensing modality can be integrated with microfluidic systems for biomedical detection and quantification. The presented sensor design, characterized by high efficiency in magnetic particle detection, can help advance next-generation point-of-care biomedical sensing systems where magnetic particles are central to biomedical assays.

Fluorescence microscopes are widely used to detect fluore-scent objects in a biological sample. However, traditional benchtop fluorescence microscopes are limited to high cost and nonportability. Recently, smartphone-based fluorescence microscopes (SFMs) have been developed, which capture images using a smartphone camera with the help of an external optical device and serve as an inexpensive and portable alternative. However, to analyze the images and get the accurate count of bioparticles, external software and computational resources are required; moreover, the lack of single and clustered count capabilities in SFMs without relying on an image library limits the use of SFMs. Here, we present an automated method as a mobile application with the ability to provide the bioparticle counts at the point of care by imaging human leukocytes and fluorescent microparticles. The application employs efficient image processing techniques that result in faster automated counting and reduced computational cost while providing comparable counts to industrial and academic standards. Compared with counting results calculated by ImageJ, a correlation coefficient of R-2 > 0.99 was observed along with the average relative error of similar to 0.5% on different kinds of fluorescent images.

Conjugating a micro/nanoparticle with the biomolecule has proved useful in diagnosing complex diseases (cancer/sepsis). The properties of the conjugating particle help in quantifying, enumerating, and detecting the target species. The conjugation technique has found its applications in diagnostics and therapy, biosensing, and sorting. The major drawback of state-of-the-art conjugation techniques is that they are time-consuming and labor-intensive. One possible solution to the problem posed by the existing conjugation technique is to use microfluidic devices. Microfluidic devices with their precise control, variable geometry, high mixing, and mass transfer efficiencies are widely used in fluid mixing applications. 3Dprinted microfluidic devices have been used to study the conjugation of particles and the detection of different cells in many state-of-the-art studies. In this study, a planer 3D printed micromixer with multiple serpentine channels was developed. The performance of the device was evaluated by performing numerical simulations and running the fluids at various flow rates through the serpentine channels. The numerical simulations produced a fluidic mixing index of ~0.96 at various mesh configurations. Similar particle conjugation efficiency was achieved in just ~3-5 minutes as compared to hours of incubation with state-of-the-art conjugation protocols.

This systematic review examined the association between depression and myocardial infarction with non-obstructive coronary arteries (MINOCA). A comprehensive literature search was conducted using electronic databases, resulting in the inclusion of six small case-control and cohort studies reported from Spain, Australia, China, and Pakistan. The studies included various study designs, such as cohort studies, case-control studies, and prospective cohort studies. The results of the systematic review indicate a significant association between depression and MINOCA. Several studies reported a higher prevalence of depression among MINOCA patients compared to those with obstructive coronary artery disease. Additionally, depression was found to be associated with worse outcomes in MINOCA patients, including increased cardiovascular events, all-cause mortality, and reduced quality of life. Some studies suggest that psychological factors, such as chronic stress, inflammation, and altered sympathetic nervous system activity, may play a role in the development and progression of MINOCA in individuals with depression. The findings highlight the importance of considering depression as a potential risk factor and prognostic marker in MINOCA patients. Early identification and management of depression in these individuals may improve outcomes and quality of life. A multi-center randomized controlled trial is needed to better understand the underlying mechanisms and to develop targeted interventions for individuals with depression and MINOCA.

Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing. Though multiple therapies have been developed to treat CML, early detection still plays a pivotal role in the overall patient survival rate. The current technologies used for CML diagnosis are costly and are confined to laboratory settings which impede their application in the point-of-care settings for early-stage detection of CML. This study provides detailed analysis and insights into the significance of CML, patient symptoms, biomarkers used for testing, and best possible detection techniques responsible for the enhancement in survival rates. A critical and detailed review is provided around potential microfluidic devices that can be adapted to detect the biomarkers associated with CML while enabling point-of-care testing for early diagnosis of CML to improve patient survival rates.

A constant threat of exposure to toxic industrial gases poses a critical concern for the safety of industrial workers. The existing commercially available gas sensing and monitoring platforms are limited in their abilities to monitor the potential leakages over a wide area and range of toxic gases. The latest advancements in technology have given birth to wearable sensing platforms that can be employed to monitor various physiological and environmental parameters. The use of these wearable devices has been constantly explored for monitoring and detection of toxic gases in the surrounding environment of a user. Unlike, traditional platforms wearable devices provide flexible, portable, and efficient solutions to the problems faced by workers in industries like mining and oil and gas. Current state-of-the-art wearable toxic gas monitoring solutions target a specific type of toxic gas and provide either an expensive or an uncomfortable device for the industrial worker. Also, these devices fail to provide useful information regarding the current health of the user. In this study, a multipurpose toxic gas sensing device has been presented along with the features of physiological signal measurement of the industrial worker. To broaden the scope of the device multiple metal oxide-based gas sensors were integrated and a custom-designed graphical user interface was designed to provide control and safety notifications to the user. Finally, a wireless connection to a custom-designed mobile application lets users analyze and report the data collected from the developed platform. Following the set of features introduced in the device, it can be used in any industrial setting as personal protective equipment to ensure the safety of the workers.

Fluorescence microscopes are commonly used to detect micro and nano biological entities (blood cells, microbes, functionalized particles etc.). Several experimental assays have been developed for biomarker measurements using this microscopy instrument. Common examples include cell viability analysis, surface receptor based specific blood cell enumeration, phagocytosis determination etc. However, the laboratory-based bench top fluorescence microscope suffers from high capital cost, maintenance, experienced staff for its operation and lack of portability. To address several of these challenges, here we have developed a hand-held, portable, 3D printed fluorescence microscope to enable single cell enumeration and nanoparticle imaging operated by smartphone. 3D printed platform is composed of top and bottom portions. The bottom portion includes excitation optics including an LED and excitation filter. While the top portion houses an emission filter, and a lens, while a smartphone can be placed on top portion for imaging. The excitation and emission filters can be swapped out for different fluorophores used. We have used the platform for imaging leukocytes collected from the whole blood. Blood samples were procured from Robert Wood Johnson Medical Hospital. We ran the leukocytes on our photonics platform and laboratory florescence microscope concurrently for a comparative analysis and found a high correlation of R2 = 0.99 in between cell counting results. Further, swapping different lenses can achieve high resolution for the imaging system. The resolution was verified by US Airforce Resolution Chart. This allowed us to image fluorescent nanoparticles up to 800nm at various concentrations and excitation modalities. This platform can be used for single-cell analysis, specific leukocytes enumeration and nanoparticles imaging. The demonstrated results can enable significant applications to develop many biomedical and diagnostic assays.

The growing need for personalized, accurate, and non-invasive diagnostic technology has resulted in significant advancements, from pushing current mechanistic limitations to innovative modality developments across various disease-related biomarkers. However, there still lacks clinical solutions for analyzing multiple biomarkers simultaneously, limiting prognosis for patients suffering with complicated diseases or comorbidities. Here, we conceived, fabricated, and validated a multifrequency impedance cytometry apparatus with novel frequency-sensitive barcoded metal oxide Janus particles (MOJPs) as cell-receptor targeting agents. These microparticles are modulated by a metal oxide semi-coating which exhibit electrical property changes in a multifrequency electric field and are functionalized to target CD11b and CD66b membrane proteins on neutrophils. A multi-modal system utilizing supervised machine learning and simultaneous high-speed video microscopy classifies immune-specific surface receptors targeted by MOJPs as they form neutrophil-MOJP conjugates, based on multivariate multifrequency electrical recordings. High precision and sensitivity were determined based on the type of MOJPs conjugated with cells (>90% accuracy between neutrophil-MOJP conjugates versus cells alone). Remarkably, the design could differentiate the number of MOJPs conjugated per cell within the same MOJP class (>80% accuracy); which also improved comparing electrical responses across different MOJP types (>75% accuracy) as well. Such trends were consistent in individual blood samples and comparing consolidated data across multiple samples, demonstrating design robustness. The configuration may further expand to include more MOJP types targeting critical biomarker receptors in one sample and increase the modality's multiplexing potential.

This study uses time-frequency transformed data and deep learning (DL) models to identify the groups of metal oxide nano-coated micro-particles using an impedance cytometer. The nano-coated bioparticles generate distinct electrical signals in a multifrequency electric field and can be used in biosensing applications. The current machine learning-enabled sensing modalities are unable to accurately differentiate different bioparticles as the feature selection and feature engineering techniques are ineffective in selecting useful and informative features. Here, we use Wigner-Vile Distribution to transform the time series data into the time-frequency domain and employ three deep learning models to evaluate the ability of time-frequency transformed data to accurately represent the most important features. A classification accuracy of 75% for (10nm and 30nm) coated particles was achieved on the simplest DL model. This combination of time-frequency representation and the DL model will be sufficient to differentiate bioparticles by acting as an alternative to other ML-based techniques.

Immune system activates in response to pathogenic infections in human bodies. One of the critical processes of the inflammatory pathway is the killing of pathogens/ microbes by specific white blood cells, called as Phagocytes. Neutrophils represent majority of phagocytes and recognize the immunoglobulin (IgG) bound bacteria with subsequent binding, internalization and finally killing. Quantifying the efficacy of the phagocytosis process will enable the personalized monitoring of patients’ immune response. This is critical for diagnostics of high-risk patients with infectious diseases in particular sepsis. Sepsis is a global health concern with major clinical diagnostics bottlenecks impacting the patients’ outcomes. In United States, more than a million patients are diagnosed with severe sepsis, out of which more than 30% die. Identifying high risk septic patients is critical to save their lives. To address this unmet need, here, we have developed a microfluidic biosensor to quantify the phagocytic activity of neutrophils using blood samples collected from hospital patients. The biosensor is composed of impedance spectroscopy based electronic sensor with integrated magnetic module. The microfluidic impedance sensor is built by fabricating co-planar gold microelectrodes on glass substrate and bonding it with PDMS based microfluidic channel. The impedance spectroscopy will generate the voltage pulses as each blood cell will flow through the sensing zone over microelectrodes. The sensing zone is coupled with the quadrupole magnetic configuration which allows generating high concentrated magnetic field lines at the start and end of the sensing zone. Further, to mimic the pathogens, IgG coated magnetic particles of similar size were used. As the blood cells will pass through the sensing zone, the magnetic particles internalized by neutrophils will experience a speed differential by magnetic modulation. The resulting electrical pulses will produce signature differences in its profile when a neutrophil with internalized magnetic particles will flow compared to non-phagocytes. We obtained multivariate data from each pulse which included pulse amplitude, width, rise/ fall times of the pulses. Further we developed a machine learning model based on artificial neural network (ANN) and fed each pulse data to classify a phagocytosis event. We collected 17 patient samples from Robert Wood Johnson Medical Hospital and ran on our microfluidic sensor. These samples were equally divided into control (with no phagocytosis) and positive (with phagocytosis) cells. Tens of thousands of blood cells from each sample were ran through our biosensor and collected data was fed to ANN model for classification. Contingency tables and receiver operating curves (ROC) were developed. ANN model was able to predict phagocytosis samples with 88% accuracy and AUC of 0.92. Similarly, lactate level measurements (provided by the hospital) were used to classify high-risk and low-risk patients (threshold of 2mm/L was used). Employing collected electronic data, our ANN model was also able to classify the patients to the appropriate risk groups with 88% accuracy and AUC of 0.85. In conclusion, we have developed microfabricated microfluidic biosensor with impedance spectroscopy and magnetic modulation. Sensor validation was done using real patient samples from the hospital. This sensor can be utilized for diagnostics applications for infectious diseases in different healthcare settings. Reference: C. Norton, U. Hassan , “Bioelectronic Sensor with Magnetic Modulation to Quantify Phagocytic Activity of Blood Cells Employing Machine Learning,” ACS Sensors , 7, 7, 1936–1945, 2022.

Absorbance spectroscopy finds many biomedical and physical applications ranging from studying the atomic and molecular details of the analyte to determination of unknown biological species and their concentration or activity in the samples. Commercially available laboratory-based spectrometers are usually bulky and require high power and laborious manual processing, making them unsuitable to be deployed in portable and space-constrained environments, thereby further limiting their utility for real-time on-site monitoring. To address these challenges, here we developed a portable 3D-printed multispectral spectrophotometer based on absorbance spectroscopy for real-time monitoring of enzyme molecular activity. Monitoring enzyme (such as tyrosinase) activity is critical, as it quantifies its reaction rate, which is dependent on many factors such as the enzyme and substrate concentrations, temperature, pH, and other regulators such as inhibitors and effectors. Tyrosinase is a critical enzyme responsible for melanin synthesis in living beings and exhibits enzymatic browning in fruits and vegetables. It finds various commercial applications in the fields of healthcare (skin pigmentation, wound healing, etc.), forensics, and food processing. Here, tyrosinase activity was monitored using a 3D-printed spectral sensor at different rates and compared against measurements obtained from laboratory instruments. The enzyme activity was also studied using kojic acid (i.e., a commonly employed commercial tyrosinase inhibitor) while varying its molar and volume concentrations to control the reaction rate at discrete activity levels. For tyrosinase activity monitoring, the fabricated device has shown significant correlation (R-2 = 0.9999) compared to measurements from the standard table-top spectrophotometer. We also provide a performance comparison between the 3D-printed and the laboratory spectrophotometer instruments by studying tyrosinase enzyme activity with and without the influence of an inhibitor. Such a device can be translated into various absorbance spectroscopy-based point-of-care biomedical and healthcare applications.

Micro-nanoparticle and leukocyte imaging find significant applications in the areas of infectious disease diagnostics, cellular therapeutics, and biomanufacturing. Portable fluorescence microscopes have been developed for these measurements, however, quantitative assessment of the quality of images (micro-nanoparticles, and leukocytes) captured using these devices remains a challenge. Here, we present a novel method for automated quality assessment of fluorescent images (AQAFI) captured using smartphone fluorescence microscopes (SFM). AQAFI utilizes novel feature extraction methods to identify and measure multiple features of interest in leukocyte and micro-nanoparticle images. For validation of AQAFI, fluorescent particles of different diameters (8.3, 2, 1, 0.8 μm) were imaged using custom-designed SFM at a range of excitation voltages (3.8-4.5 V). Particle intensity, particle vicinity intensity, and image background noise were chosen as analytical parameters of interest and measured by the AQAFI algorithm. A control method was developed by manual calculation of these parameters using ImageJ which was subsequently used to validate the performance of the AQAFI method. For micro-nanoparticle images, correlation coefficients with R2 > 0.95 were obtained for each parameter of interest while comparing AQAFI vs. control (ImageJ). Subsequently, key performance indicators (KPIs) i.e., signal difference to noise ratio (SDNR) and contrast to noise ratio (CNR) were defined and calculated for these micro-nano particle images using both AQAFI and control methods. Finally, we tested the performance of the AQAFI method on the fluorescent images of human peripheral blood leukocytes captured using our custom SFM. Correlation coefficients of R2 = 0.99 were obtained for each parameter of interest (leukocyte intensity, vicinity intensity, background noise) calculated using AQAFI and control (ImageJ). A high correlation was also found between the CNR and SDNR values calculated using both methods. The developed AQAFI method thus presents an automated and precise way to quantify and assess the quality of fluorescent images (micro-nano particles and leukocytes) captured using portable SFMs. Similarly, this study finds broader applicability and can also be employed with benchtop microscopes for the quantitative assessment of their imaging performance.