Digital diffraction analysis enables low-cost molecular diagnostics on a smartphone

Domain-Agnostic Representation of Side-Channels

Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human-computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established fields of advanced sensing and signal processing underlie each example, enabling the unification of these currently otherwise isolated domains. Identified themes are collating under a proposed domain-agnostic SCS framework. This SCS framework enables a formalised and systematic approach to studying, detecting, and exploiting of side channels both within and between domains. Opportunities exist for modelling SCS as data structures, allowing for computation irrespective of domain. Future methodologies can take such data structures to enable cross- and intra-domain transferability of extraction techniques, perform side-channel leakage detection, and discover new side channels within target systems.

Deep learning-assisted smartphone-based quantitative microscopy for label-free peripheral blood smear analysis

Hematologists evaluate alterations in blood cell enumeration and morphology to confirm peripheral blood smear findings through manual microscopic examination. However, routine peripheral blood smear analysis is both time-consuming and labor-intensive. Here, we propose using smartphone-based autofluorescence microscopy (Smart-AM) for imaging label-free blood smears at subcellular resolution with automatic hematological analysis. Smart-AM enables rapid and label-free visualization of morphological features of normal and abnormal blood cells (including leukocytes, erythrocytes, and thrombocytes). Moreover, assisted with deep-learning algorithms, this technique can automatically detect and classify different leukocytes with high accuracy, and transform the autofluorescence images into virtual Giemsa-stained images which show clear cellular features. The proposed technique is portable, cost-effective, and user-friendly, making it significant for broad point-of-care applications.

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

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.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Smartphone-based diagnostics for biosensing infectious human pathogens

The widespread usage of smartphones has made accessing vast troves of data easier for everyone. Smartphones are powerful, handy, and easy to operate, making them a valuable tool for improving public health through diagnostics. When combined with other devices and sensors, smartphones have shown potential for detecting, visualizing, collecting, and transferring data, enabling rapid disease diagnosis. In resource-limited settings, the user-friendly operating system of smartphones allows them to function as a point-of-care platform for healthcare and disease diagnosis. Herein, we critically reviewed the smartphone-based biosensors for the diagnosis and detection of diseases caused by infectious human pathogens, such as deadly viruses, bacteria, and fungi. These biosensors use several analytical sensing methods, including microscopic imaging, instrumental interface, colorimetric, fluorescence, and electrochemical biosensors. We have discussed the diverse diagnosis strategies and analytical performances of smartphone-based detection systems in identifying infectious human pathogens, along with future perspectives.

Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

Flash Characterization of Smartphones Used in Point-of-Care Diagnostics

Rapidly growing interest in smartphone cameras as the basis of point-of-need diagnostic and bioanalytical technologies increases the importance of quantitative characterization of phone optical performance under real-world operating conditions. In the context of our development of lateral-flow immunoassays based on phosphorescent nanoparticles, we have developed a suite of tools for characterizing the temporal and spectral profiles of smartphone torch and flash emissions, and their dependence on phone power state. In this work, these tools are described and documented to make them easily available to others, and demonstrated by application to characterization of Apple iPhone 5s, iPhone 6s, iPhone 8, iPhone XR, and Samsung Note8 flash performance as a function of time and wavelength, at a variety of power settings. Flash and torch intensity and duration vary with phone state and among phone models. Flash has high variability when the battery charge is below 10%, thus, smartphone-based Point-of-Care (POC) tests should only be performed at a battery level of at least 15%. Some output variations could substantially affect the results of assays that rely on the smartphone flash.

Multiplexed imaging in oncology

In oncology, technologies for clinical molecular imaging are used to diagnose patients, establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed methods increase the number of disease-specific biomarkers that can be detected simultaneously, such as the overexpression of oncogenic proteins, aberrant metabolite uptake and anomalous blood perfusion. The quantitative localization of each biomarker could considerably increase the specificity and the accuracy of technologies for clinical molecular imaging to facilitate granular diagnoses, patient stratification and earlier assessments of the responses to administered therapeutics. In this Review, we discuss established techniques for multiplexed imaging and the most promising emerging multiplexing technologies applied to the imaging of isolated tissues and cells and to non-invasive whole-body imaging. We also highlight advances in radiology that have been made possible by multiplexed imaging.

Computational Optics for Point-of-Care Breast Cancer Profiling

With the global burden of cancer on the rise, it is critical to developing new modalities that could detect cancer and guide targeted treatments in fast and inexpensive ways. The need for such technologies is vital, especially in underserved regions where severe diagnostic bottlenecks exist. Recently, we developed a low-cost digital diagnostic system for breast cancer using fine-needle aspirates (FNAs). Named, AIDA (artificial intelligence diffraction analysis), the system combines lens-free digital diffraction imaging with deep-learning algorithms to achieve automated, rapid, and high-throughput cellular analyses for breast cancer diagnosis of FNA and subtype classification for better-guided treatments (Min et al. ACS Nano 12:9081-9090, 2018). Although primarily validated for breast cancer and lymphoma (Min et al. ACS Nano 12:9081-9090, 2018; Im et al. Nat Biomed Eng 2:666-674, 2018), the system could be easily adapted to diagnosing other prevalent cancers and thus find widespread use for global health.

Diagnostic techniques in HPV infections and the need to implement them in plantar lesions: A systematic review

INTRODUCTION

Human papillomavirus has been reported as the etiological cause of most cervical cancers and other potentially malignant lesions. It also affects other areas, producing benign tumors on the skin. Plantar warts are a common problem found in clinical practice and share signs and symptoms with other dermatological conditions. Diagnosis of HPV infection remains a hot topic in research.

METHOD

The present work systematically reviews the literature on the diagnostic techniques available in the clinical setting for any type of lesion produced by the virus and compares the techniques identified to those found in use for foot lesions.

RESULTS

Results showed a variety of diagnostic methods, including molecular techniques, which exhibit more sensitivity than other methods but are less frequently applied to plantar lesions, where visual inspection is the most frequent method but can lead to errors.

CONCLUSION

The techniques identified need to be applied to plantar lesions to improve differential diagnosis in clinical practice.

EXPERT OPINION

Research will continue to grow and a proper diagnostic technique for plantar lesions will be available in the near future.

A Smartphone-Fluidic Digital Imaging Analysis System for Pancreatic Islet Mass Quantification

Islet beta-cell viability, function, and mass are three decisive attributes that determine the efficacy of human islet transplantation for type 1 diabetes mellitus (T1DM) patients. Islet mass is commonly assessed manually, which often leads to error and bias. Digital imaging analysis (DIA) system has shown its potential as an alternative, but it has some associated limitations. In this study, a Smartphone-Fluidic Digital Imaging Analysis (SFDIA) System, which incorporates microfluidic techniques and Python-based video processing software, was developed for islet mass assessment. We quantified islets by tracking multiple moving islets in a microfluidic channel using the SFDIA system, and we achieved a relatively consistent result. The counts from the SFDIA and manual counting showed an average difference of 2.91 ± 1.50%. Furthermore, our software can analyze and extract key human islet mass parameters, including quantity, size, volume, IEq, morphology, and purity, which are not fully obtainable from traditional manual counting methods. Using SFDIA on a representative islet sample, we measured an average diameter of 99.88 ± 53.91 µm, an average circularity of 0.591 ± 0.133, and an average solidity of 0.853 ± 0.107. Via analysis of dithizone-stained islets using SFDIA, we found that a higher islet tissue percentage is associated with top-layer islets as opposed to middle-layer islets (0.735 ± 0.213 and 0.576 ± 0.223, respectively). Our results indicate that the SFDIA system can potentially be used as a multi-parameter islet mass assay that is superior in accuracy and consistency, when compared to conventional manual techniques.

Digital Health Strategies for Cervical Cancer Control in Low- and Middle-Income Countries: Systematic Review of Current Implementations and Gaps in Research

BACKGROUND

Nearly 90% of deaths due to cervical cancer occur in low- and middle-income countries (LMICs). In recent years, many digital health strategies have been implemented in LMICs to ameliorate patient-, provider-, and health system-level challenges in cervical cancer control. However, there are limited efforts to systematically review the effectiveness and current landscape of digital health strategies for cervical cancer control in LMICs.

OBJECTIVE

We aim to conduct a systematic review of digital health strategies for cervical cancer control in LMICs to assess their effectiveness, describe the range of strategies used, and summarize challenges in their implementation.

METHODS

A systematic search was conducted to identify publications describing digital health strategies for cervical cancer control in LMICs from 5 academic databases and Google Scholar. The review excluded digital strategies associated with improving vaccination coverage against human papillomavirus. Titles and abstracts were screened, and full texts were reviewed for eligibility. A structured data extraction template was used to summarize the information from the included studies. The risk of bias and data reporting guidelines for mobile health were assessed for each study. A meta-analysis of effectiveness was planned along with a narrative review of digital health strategies, implementation challenges, and opportunities for future research.

RESULTS

In the 27 included studies, interventions for cervical cancer control focused on secondary prevention (ie, screening and treatment of precancerous lesions) and digital health strategies to facilitate patient education, digital cervicography, health worker training, and data quality. Most of the included studies were conducted in sub-Saharan Africa, with fewer studies in other LMIC settings in Asia or South America. A low risk of bias was found in 2 studies, and a moderate risk of bias was found in 4 studies, while the remaining 21 studies had a high risk of bias. A meta-analysis of effectiveness was not conducted because of insufficient studies with robust study designs and matched outcomes or interventions.

CONCLUSIONS

Current evidence on the effectiveness of digital health strategies for cervical cancer control is limited and, in most cases, is associated with a high risk of bias. Further studies are recommended to expand the investigation of digital health strategies for cervical cancer using robust study designs, explore other LMIC settings with a high burden of cervical cancer (eg, South America), and test a greater diversity of digital strategies.

Biological characteristics and biomarkers of novel SARS-CoV-2 facilitated rapid development and implementation of diagnostic tools and surveillance measures

Existing coronavirus named as a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has speeded its spread across the globe immediately after emergence in China, Wuhan region, at the end of the year 2019. Different techniques, including genome sequencing, structural feature classification by electron microscopy, and chest imaging using computed tomography, are primarily used to diagnose and screen SARS-CoV-2 suspected individuals. Determination of the viral structure, surface proteins, and genome sequence has provided a design blueprint for the diagnostic investigations of novel SARS-CoV-2 virus and rapidly emerging diagnostic technologies, vaccine trials, and cell-entry-inhibiting drugs. Here, we describe recent understandings on the spike glycoprotein (S protein), receptor-binding domain (RBD), and angiotensin-converting enzyme 2 (ACE2) and their receptor complex. This report also aims to review recently established diagnostic technologies and developments in surveillance measures for SARS-CoV-2 as well as the characteristics and performance of emerging techniques. Smartphone apps for contact tracing can help nations to conduct surveillance measures before a vaccine and effective medicines become available. We also describe promising point-of-care (POC) diagnostic technologies that are under consideration by researchers for advancement beyond the proof-of-concept stage. Developing novel diagnostic techniques needs to be facilitated to establish automatic systems, without any personal involvement or arrangement to curb an existing SARS-CoV-2 epidemic crisis, and could also be appropriate for avoiding the emergence of a future epidemic crisis.

Navigating the Pandemic Response Life Cycle: Molecular Diagnostics and Immunoassays in the Context of COVID-19 Management

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To counter COVID-19 spreading, an infrastructure to provide rapid and thorough molecular diagnostics and serology testing is the cornerstone of outbreak and pandemic management. We hereby review the clinical insights with regard to using molecular tests and immunoassays in the context of COVID-19 management life cycle: the preventive phase, the preparedness phase, the response phase and the recovery phase. The spatial and temporal distribution of viral RNA, antigens and antibodies during human infection is summarized to provide a biological foundation for accurate detection of the disease. We shared the lessons learned and the obstacles encountered during real world high-volume screening programs. Clinical needs are discussed to identify existing technology gaps in these tests. Leverage technologies, such as engineered polymerases, isothermal amplification, and direct amplification from complex matrices may improve the productivity of current infrastructure, while emerging technologies like CRISPR diagnostics, visual end point detection, and PCR free methods for nucleic acid sensing may lead to at-home tests. The lessons learned, and innovations spurred from the COVID-19 pandemic could upgrade our global public health infrastructure to better combat potential outbreaks in the future.

Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial ways to recover subtle, hidden or unexpected information. Practical examples of side-channel sensing are well known in domains such as cybersecurity (CYB), but are not formally recognised within the domain of medical diagnostics and monitoring (MDM). This article reviews side-channel usage within CYB and MDM, identifying techniques and methodologies applicable to both domains. We establish a systematic structure for the use of side-channel sensing in MDM that is comparable to existing structures in CYB, and promote cross-domain transferability of knowledge, mindsets, and techniques.

Current and Perspective Diagnostic Techniques for COVID-19

Since late December 2019, the coronavirus pandemic (COVID-19; previously known as 2019-nCoV) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been surging rapidly around the world. With more than 1,700,000 confirmed cases, the world faces an unprecedented economic, social, and health impact. The early, rapid, sensitive, and accurate diagnosis of viral infection provides rapid responses for public health surveillance, prevention, and control of contagious diffusion. More than 30% of the confirmed cases are asymptomatic, and the high false-negative rate (FNR) of a single assay requires the development of novel diagnostic techniques, combinative approaches, sampling from different locations, and consecutive detection. The recurrence of discharged patients indicates the need for long-term monitoring and tracking. Diagnostic and therapeutic methods are evolving with a deeper understanding of virus pathology and the potential for relapse. In this Review, a comprehensive summary and comparison of different SARS-CoV-2 diagnostic methods are provided for researchers and clinicians to develop appropriate strategies for the timely and effective detection of SARS-CoV-2. The survey of current biosensors and diagnostic devices for viral nucleic acids, proteins, and particles and chest tomography will provide insight into the development of novel perspective techniques for the diagnosis of COVID-19.

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

BACKGROUND

Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations.

OBJECTIVE

In this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians' interpretation.

METHODS

A smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA.

RESULTS

An average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA.

CONCLUSIONS

We explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

Point-of-care cervical cancer screening using deep learning-based microholography

Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap.

Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed.

Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay.

Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations.

Compact and Filter-Free Luminescence Biosensor for Mobile in Vitro Diagnoses

We report a sensitive and versatile biosensing approach, LUCID (luminescence compact in vitro diagnostics), for quantitative molecular and cellular analyses. LUCID uses upconversion nanoparticles (UCNPs) as luminescent reporters in mutually exclusive photoexcitation and read-out sequences implemented on a smartphone. The strategy improves imaging signal-to-noise ratios, eliminating interference from excitation sources and minimizing autofluorescence, and thus enables filterless imaging. Here we developed a miniaturized detection system and optimized UCNPs for the system and biological applications. Nanoparticle luminescence lifetime was extended by controlling particle structure and composition. When tested with a range of biological targets, LUCID achieved high detection sensitivity (0.5 pM for protein and 0.1 pM for nucleic acids), differentiated bacterial samples, and allowed profiling of cells. In proof-of-concept clinical use, LUCID demonstrated effective screening of cancer cells in cervical brushing specimens, identifying patients at high risk for malignancy. These results suggest that LUCID could serve as a broadly applicable and inexpensive diagnostic platform.

Low-cost, high-speed near infrared reflectance confocal microscope

We have developed a low-cost, near-infrared (NIR) reflectance confocal microscope (RCM) to overcome challenges in the imaging depth and speed found in our previously-reported smartphone confocal microscope. In the new NIR RCM device, we have used 840 nm superluminescent LED (sLED) to increase the tissue imaging depth and speed. A new confocal detection optics has been developed to maintain high lateral resolution even when a relatively large slit width was used. The material cost of the NIR RCM device was still low, ~$5,200. The lateral resolution was 1.1 µm and 1.3 µm along the vertical and horizontal directions, respectively. Axial resolution was measured as 11.2 µm. In vivo confocal images of human forearm skin obtained at the imaging speed of 203 frames/sec clearly visualized characteristic epidermal and dermal cellular features of the human skin.

Fluorescent microbeads for point-of-care testing: a review

Microbead-based point-of-care testing (POCT) has demonstrated great promise in translating detection modalities from bench-side to bed-side. This is due to the ease of visualization, high surface area-to-volume ratio of beads for efficient target binding, and efficient encoding capability for simultaneous detection of multiple analytes. This review (with 112 references) summarizes the progress made in the field of fluorescent microbead-based POCT. Following an introduction into the field, a first large section sums up techniques and materials for preparing microbeads, typically of dye-labelled particles, various kinds of quantum dots and upconversion materials. Further subsections cover the encapsulation of nanoparticles into microbeads, decoration of nanoparticles on microbeads, and in situ embedding of nanoparticles during microbead synthesis. A next large section summarizes microbead-based fluorometric POCT, with subsections on detection of nucleic acids, proteins, circulating tumor cells and bacteria. A further section covers emerging POCT based on the use of smartphones or flexible microchips. The last section gives conclusions and an outlook on current challenges and possible solutions. Aside from giving an overview on the state of the art, we expect this article to boost the further development of POCT technology. Graphical Abstract Schematic presentation of the fabrication of microbeads, the detection targets of interest including bacteria, circulating tumor cells (CTCs), protein and nucleic acid, and the emerging point-of-care testing (POCT) platform. The colored wheels of the bus represent the fluorescent materials embedded in (red color) or decorated on the surface of microbeads (green color).

Inorganic Complexes and Metal-Based Nanomaterials for Infectious Disease Diagnostics

Infectious diseases claim millions of lives each year. Robust and accurate diagnostics are essential tools for identifying those who are at risk and in need of treatment in low-resource settings. Inorganic complexes and metal-based nanomaterials continue to drive the development of diagnostic platforms and strategies that enable infectious disease detection in low-resource settings. In this review, we highlight works from the past 20 years in which inorganic chemistry and nanotechnology were implemented in each of the core components that make up a diagnostic test. First, we present how inorganic biomarkers and their properties are leveraged for infectious disease detection. In the following section, we detail metal-based technologies that have been employed for sample preparation and biomarker isolation from sample matrices. We then describe how inorganic- and nanomaterial-based probes have been utilized in point-of-care diagnostics for signal generation. The following section discusses instrumentation for signal readout in resource-limited settings. Next, we highlight the detection of nucleic acids at the point of care as an emerging application of inorganic chemistry. Lastly, we consider the challenges that remain for translation of the aforementioned diagnostic platforms to low-resource settings.

Learning-based automatic sensing and size classification of microparticles using smartphone holographic microscopy

A microparticle classifier is established by synergetic integration of smartphone-based digital in-line holographic microscopy and supervised machine learning.

Smartphone-based clinical diagnostics: towards democratization of evidence-based health care

Recent advancements in bioanalytical techniques have led to the development of novel and robust diagnostic approaches that hold promise for providing optimal patient treatment, guiding prevention programs and widening the scope of personalized medicine. However, these advanced diagnostic techniques are still complex, expensive and limited to centralized healthcare facilities or research laboratories. This significantly hinders the use of evidence-based diagnostics for resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients, leaving these populations without access to precision and quality medicine. Smartphone-based imaging and sensing platforms are emerging as promising alternatives for bridging this gap and decentralizing diagnostic tests offering practical features such as portability, cost-effectiveness and connectivity. Moreover, towards simplifying and automating bioanalytical techniques, biosensors and lab-on-a-chip technologies have become essential to interface and integrate these assays, bringing together the high precision and sensitivity of diagnostic techniques with the connectivity and computational power of smartphones. Here, we provide an overview of the emerging field of clinical smartphone diagnostics and its contributing technologies, as well as their wide range of areas of application, which span from haematology to digital pathology and rapid infectious disease diagnostics.

Label-Free High-Throughput Leukemia Detection by Holographic Microscopy

Complete blood count and differentiation of leukocytes (DIFF) belong to the most frequently performed laboratory diagnostic tests. Here, a flow cytometry-based method for label-free DIFF of untouched leukocytes by digital holographic microscopy on the rich phase contrast of peripheral leukocyte images, using highly controlled 2D hydrodynamic focusing conditions is reported. Principal component analysis of morphological characteristics of the reconstructed images allows classification of nine leukocyte types, in addition to different types of leukemia and demonstrates disappearance of acute myeloid leukemia cells in remission. To exclude confounding effects, the classification strategy is tested by the analysis of 20 blinded clinical samples. Here, 70% of the specimens are correctly classified with further 20% classifications close to a correct diagnosis. Taken together, the findings indicate a broad clinical applicability of the cytometry method for automated and reagent-free diagnosis of hematological disorders.

Deep transfer learning-based hologram classification for molecular diagnostics

Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH. This limits LDIH utility for point-of-care applications, particularly in resource limited settings. We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance with experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics.

Developing a Roadmap for Interventional Oncology

Interventional oncology uses image-guided procedures to enhance cancer care. Today, this specialty plays an increasingly critical role in cancer diagnosis (e.g., biopsy), cancer therapy (e.g., ablation or embolization), and cancer symptom palliation (e.g., nephrostomies or biliary drainages). Although the number of procedures and technical capabilities has improved over the last few years, challenges remain. In this article we discuss the need to advance existing procedures, develop new ones, and focus on several operational aspects that will dictate future interventional techniques to enhance cancer care, particularly by accelerating drug development and improving patient outcomes.

IMPLICATIONS FOR PRACTICE

Interventional oncology is vital for cancer diagnosis, therapy, and symptom palliation. This report focuses on current interventional procedures and techniques with a look toward future improvements that will improve cancer care and patient outcomes.

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings

The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid, and treatment-informative diagnostics. On the basis of advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning-based analysis of sample aspirates. The image algorithm is fast, enabling cellular analyses at high throughput (∼3 s per 1000 cells), and the unsupervised processing allows use by lower skill health care workers. AIDA can perform quantitative molecular profiling on individual cells, revealing intratumor molecular heterogeneity, and has the potential to improve cancer diagnosis and treatment. The system could be further developed for other cancers and thus find widespread use in global health.

Smartphone-based pathogen diagnosis in urinary sepsis patients

Background

There is an urgent need for rapid, sensitive, and affordable diagnostics for microbial infections at the point-of-care. Although a number of innovative systems have been reported that transform mobile phones into potential diagnostic tools, the translational challenge to clinical diagnostics remains a significant hurdle to overcome.

Methods

A smartphone-based real-time loop-mediated isothermal amplification (smaRT-LAMP) system was developed for pathogen ID in urinary sepsis patients. The free, custom-built mobile phone app allows the phone to serve as a stand-alone device for quantitative diagnostics, allowing the determination of genome copy-number of bacterial pathogens in real time.

Findings

A head-to-head comparative bacterial analysis of urine from sepsis patients revealed that the performance of smaRT-LAMP matched that of clinical diagnostics at the admitting hospital in a fraction of the time (~1 h vs. 18–28 h). Among patients with bacteremic complications of their urinary sepsis, pathogen ID from the urine matched that from the blood – potentially allowing pathogen diagnosis shortly after hospital admission. Additionally, smaRT-LAMP did not exhibit false positives in sepsis patients with clinically negative urine cultures.

Interpretation

The smaRT-LAMP system is effective against diverse Gram-negative and -positive pathogens and biological specimens, costs less than $100 US to fabricate (in addition to the smartphone), and is configurable for the simultaneous detection of multiple pathogens. SmaRT-LAMP thus offers the potential to deliver rapid diagnosis and treatment of urinary tract infections and urinary sepsis with a simple test that can be performed at low cost at the point-of-care.

Fund

National Institutes of Health, Chan-Zuckerberg Biohub, Bill and Melinda Gates Foundation.

Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning

The identification of patients with aggressive cancer who require immediate therapy is a health challenge in low-income and middle-income countries. Limited pathology resources, high healthcare costs and large-case loads call for the development of advanced standalone diagnostics. Here, we report and validate an automated, low-cost point-of-care device for the molecular diagnosis of aggressive lymphomas. The device uses contrast-enhanced microholography and a deep-learning algorithm to directly analyse percutaneously obtained fine-needle aspirates. We show the feasibility and high accuracy of the device in cells, as well as the prospective validation of the results in 40 patients clinically referred for image-guided aspiration of nodal mass lesions suspicious for lymphoma. Automated analysis of human samples with the portable device should allow for the accurate classification of patients with benign and malignant adenopathy.

Ultrabright Polymer-Dot Transducer Enabled Wireless Glucose Monitoring via a Smartphone

Optical methods such as absorptiometry, fluorescence, and surface plasmon resonance have long been explored for sensing glucose. However, these schemes have not had the clinical success of electrochemical methods for point-of-care testing because of the limited performance of optical sensors and the bulky instruments they require. Here, we show that an ultrasensitive optical transducer can be used for wireless glucose monitoring via a smartphone. The optical transducer combines oxygen-sensitive polymer dots (Pdots) with glucose oxidase that sensitively detect glucose when oxygen is consumed in the glucose oxidation reaction. By judicious design of the Pdots with ultralong phosphorescence lifetime, the transducer exhibited a significantly enhanced sensitivity by 1 order of magnitude as compared to the one in a previous study. As a result, the optical images of subcutaneous glucose level obtained with the smartphone camera could be utilized to clearly distinguish between euglycemia and hyperglycemia. We further developed an image processing algorithm and a software application that was installed on a smartphone. Real-time dynamic glucose monitoring in live mice was demonstrated with the smartphone and the implanted Pdot transducer.

On-Chip Cell Staining and Counting Platform for the Rapid Detection of Blood Cells in Cerebrospinal Fluid

Counting blood cells in cerebrospinal fluid (CSF) is indispensable for diagnosing several pathological conditions in the central nervous system, such as meningitis, even though collecting CSF samples is invasive. Cell counting methods, such as hemocytometer chambers and flow cytometers, have been used for CSF cell counting, but they often lack the sensitivity to detect low blood cell numbers. They also depend on off-chip, manual sample preparation or require bulky, costly equipment, thereby limiting their clinical utility. Here, we present a portable cell counting platform for simple, rapid CSF cell counting that integrates a microfluidic cell counting chamber with a miniaturized microscope. The microfluidic chamber is designed not only to be a reagent container for on-chip cell staining but also to have a large control volume for accurate cell counting. The proposed microscope miniaturizes both bright-field and fluorescence microscopy with a simple optical setup and a custom cell-counting program, thereby allowing rapid and automated cell counting of nucleated white blood cells and non-nucleated red blood cells in fluorescence and bright-field images. Using these unique features, we successfully demonstrate the ability of our counting platform to measure low CSF cell counts without sample preparation.

Rapid diagnostic testing platform for iron and vitamin A deficiency

Micronutrient deficiencies such as those of vitamin A and iron affect a third of the world's population with consequences such as night blindness, higher child mortality, anemia, poor pregnancy outcomes, and reduced work capacity. Many efforts to prevent or treat these deficiencies are hampered by the lack of adequate, accessible, and affordable diagnostic methods that can enable better targeting of interventions. In this work, we demonstrate a rapid diagnostic test and mobile enabled platform for simultaneously quantifying iron (ferritin), vitamin A (retinol-binding protein), and inflammation (C-reactive protein) status. Our approach, enabled by combining multiple florescent markers and immunoassay approaches on a single test, allows us to provide accurate quantification in 15 min even though the physiological range of the markers of interest varies over five orders of magnitude. We report sensitivities of 88%, 100%, and 80% and specificities of 97%, 100%, and 97% for iron deficiency (ferritin <15 ng/mL or 32 pmol/L), vitamin A deficiency (retinol-binding protein <14.7 μg/mL or 0.70 μmol/L) and inflammation status (C-reactive protein >3.0 μg/mL or 120 nmol/L), respectively. This technology is suitable for point-of-care use in both resource-rich and resource-limited settings and can be read either by a standard laptop computer or through our previously developed NutriPhone technology. If implemented as either a population-level screening or clinical diagnostic tool, we believe this platform can transform nutritional status assessment and monitoring globally.

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

As the burden of non-communicable diseases such as cancer continues to rise in low- and middle-income countries (LMICs), it is essential to identify and invest in promising solutions for cancer control and treatment. Point-of-care technologies (POCTs) have played critical roles in curbing infectious disease epidemics in both high- and low-income settings, and their successes can serve as a model for transforming cancer care in LMICs, where access to traditional clinical resources is often limited. The versatility, cost-effectiveness, and simplicity of POCTs warrant attention for their potential to revolutionize cancer detection, diagnosis, and treatment. This paper reviews the landscape of affordable POCTs for cancer care in LMICs with a focus on imaging tools, in vitro diagnostics, and treatment technologies and aspires to encourage innovation and further investment in this space.

Expansion Mini-Microscopy: An Enabling Alternative in Point-of-Care Diagnostics

Diagnostics play a significant role in health care. In the developing world and low-resource regions the utility for point-of-care (POC) diagnostics becomes even greater. This need has long been recognized, and diagnostic technology has seen tremendous progress with the development of portable instrumentation such as miniature imagers featuring low complexity and cost. However, such inexpensive devices have not been able to achieve a resolution sufficient for POC detection of pathogens at very small scales, such as single-cell parasites, bacteria, fungi, and viruses. To this end, expansion microscopy (ExM) is a recently developed technique that, by physically expanding preserved biological specimens through a chemical process, enables super-resolution imaging on conventional microscopes and improves imaging resolution of a given microscope without the need to modify the existing microscope hardware. Here we review recent advances in ExM and portable imagers, respectively, and discuss the rational combination of the two technologies, that we term expansion mini-microscopy (ExMM). In ExMM, the physical expansion of a biological sample followed by imaging on a mini-microscope achieves a resolution as high as that attainable by conventional high-end microscopes imaging non-expanded samples, at significant reduction in cost. We believe that this newly developed ExMM technique is likely to find widespread applications in POC diagnostics in resource-limited and remote regions by expanded-scale imaging of biological specimens that are otherwise not resolvable using low-cost imagers.

Smartphone based hand-held quantitative phase microscope using the transport of intensity equation method

In order to realize high contrast imaging with portable devices for potential mobile healthcare, we demonstrate a hand-held smartphone based quantitative phase microscope using the transport of intensity equation method. With a cost-effective illumination source and compact microscope system, multi-focal images of samples can be captured by the smartphone's camera via manual focusing. Phase retrieval is performed using a self-developed Android application, which calculates sample phases from multi-plane intensities via solving the Poisson equation. We test the portable microscope using a random phase plate with known phases, and to further demonstrate its performance, a red blood cell smear, a Pap smear and monocot root and broad bean epidermis sections are also successfully imaged. Considering its advantages as an accurate, high-contrast, cost-effective and field-portable device, the smartphone based hand-held quantitative phase microscope is a promising tool which can be adopted in the future in remote healthcare and medical diagnosis.

Holographic Assessment of Lymphoma Tissue (HALT) for Global Oncology Field Applications

Low-cost, rapid and accurate detection technologies are key requisites to cope with the growing global cancer challenges. The need is particularly pronounced in resource-limited settings where treatment opportunities are often missed due to the absence of timely diagnoses. We herein describe a Holographic Assessment of Lymphoma Tissue (HALT) system that adopts a smartphone as the basis for molecular cancer diagnostics. The system detects malignant lymphoma cells labeled with marker-specific microbeads that produce unique holographic signatures. Importantly, we optimized HALT to detect lymphomas in fine-needle aspirates from superficial lymph nodes, procedures that align with the minimally invasive biopsy needs of resource-constrained regions. We equipped the platform to directly address the practical needs of employing novel technologies for "real world" use. The HALT assay generated readouts in <1.5 h and demonstrated good agreement with standard cytology and surgical pathology.

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

Resource-poor countries and regions require effective, low-cost diagnostic devices for accurate identification and diagnosis of health conditions. Optical detection technologies used for many types of biological and clinical analysis can play a significant role in addressing this need, but must be sufficiently affordable and portable for use in global health settings. Most current clinical optical imaging technologies are accurate and sensitive, but also expensive and difficult to adapt for use in these settings. These challenges can be mitigated by taking advantage of affordable consumer electronics mobile devices such as webcams, mobile phones, charge-coupled device (CCD) cameras, lasers, and LEDs. Low-cost, portable multi-wavelength fluorescence plate readers have been developed for many applications including detection of microbial toxins such as C. Botulinum A neurotoxin, Shiga toxin, and S. aureus enterotoxin B (SEB), and flow cytometry has been used to detect very low cell concentrations. However, the relatively low sensitivities of these devices limit their clinical utility. We have developed several approaches to improve their sensitivity presented here for webcam based fluorescence detectors, including (1) image stacking to improve signal-to-noise ratios; (2) lasers to enable fluorescence excitation for flow cytometry; and (3) streak imaging to capture the trajectory of a single cell, enabling imaging sensors with high noise levels to detect rare cell events. These approaches can also help to overcome some of the limitations of other low-cost optical detection technologies such as CCD or phone-based detectors (like high noise levels or low sensitivities), and provide for their use in low-cost medical diagnostics in resource-poor settings.

Sparsity-Based Pixel Super Resolution for Lens-Free Digital In-line Holography

Lens-free digital in-line holography (LDIH) is a promising technology for portable, wide field-of-view imaging. Its resolution, however, is limited by the inherent pixel size of an imaging device. Here we present a new computational approach to achieve sub-pixel resolution for LDIH. The developed method is a sparsity-based reconstruction with the capability to handle the non-linear nature of LDIH. We systematically characterized the algorithm through simulation and LDIH imaging studies. The method achieved the spatial resolution down to one-third of the pixel size, while requiring only single-frame imaging without any hardware modifications. This new approach can be used as a general framework to enhance the resolution in nonlinear holographic systems.

Digital diffraction detection of protein markers for avian influenza

Rapid pathogen testing is expected to play a critical role in infection control and in limiting epidemics. Smartphones equipped with state-of-the-art computing and imaging technologies have emerged as new point-of-use (POU) sensing platforms. We herein report a new assay format for fast, sensitive and portable detection of avian influenza-associated antibodies.