Malaria Diagnosis Using a Mobile Phone Polarized Microscope

Development of a low-cost robotized 3D-prototype for automated optical microscopy diagnosis: An open-source system

In a clinical context, conventional optical microscopy is commonly used for the visualization of biological samples for diagnosis. However, the availability of molecular techniques and rapid diagnostic tests are reducing the use of conventional microscopy, and consequently the number of experienced professionals starts to decrease. Moreover, the continuous visualization during long periods of time through an optical microscope could affect the final diagnosis results due to induced human errors and fatigue. Therefore, microscopy automation is a challenge to be achieved and address this problem. The aim of the study is to develop a low-cost automated system for the visualization of microbiological/parasitological samples by using a conventional optical microscope, and specially designed for its implementation in resource-poor settings laboratories. A 3D-prototype to automate the majority of conventional optical microscopes was designed. Pieces were built with 3D-printing technology and polylactic acid biodegradable material with Tinkercad/Ultimaker Cura 5.1 slicing softwares. The system's components were divided into three subgroups: microscope stage pieces, storage/autofocus-pieces, and smartphone pieces. The prototype is based on servo motors, controlled by Arduino open-source electronic platform, to emulate the X-Y and auto-focus (Z) movements of the microscope. An average time of 27.00 ± 2.58 seconds is required to auto-focus a single FoV. Auto-focus evaluation demonstrates a mean average maximum Laplacian value of 11.83 with tested images. The whole automation process is controlled by a smartphone device, which is responsible for acquiring images for further diagnosis via convolutional neural networks. The prototype is specially designed for resource-poor settings, where microscopy diagnosis is still a routine process. The coalescence between convolutional neural network predictive models and the automation of the movements of a conventional optical microscope confer the system a wide range of image-based diagnosis applications. The accessibility of the system could help improve diagnostics and provide new tools to laboratories worldwide.

Efficient Malaria Parasite Detection From Diverse Images of Thick Blood Smears for Cross-Regional Model Accuracy

Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating smartphones with microscopes. This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. Methods: This approach combines image segmentation and a convolutional neural network (CNN) to distinguish between white blood cells, artifacts, and malaria parasites. A portable system integrates a microscope with a graphical user interface to facilitate rapid malaria detection from smartphone images. We trained the CNN model using open-source data from the Chittagong Medical College Hospital, Bangladesh. Results: The validation process, using microscopic TBS from both the training dataset and an additional dataset from Sub-Saharan Africa, demonstrated that the proposed model achieved an accuracy of 97.74% ± 0.05% and an F1-score of 97.75% ± 0.04%. Remarkably, our proposed model with AlexNet surpasses the reported literature performance of 96.32%. Conclusions: This algorithm shows promise in aiding malaria-stricken regions, especially those with limited resources.

AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images

Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.

A high-performance cell-phone based polarized microscope for malaria diagnosis

We present a cell-phone based polarized microscope for diagnosing malaria through hemozoin recognition over a wide field-of-view (FOV) accompanied with decent image performance. The system is constructed based on attachment method using a lens assembly as objective, two mobile phones and two linear polarizers. A ~0.92 μm resolution across a FOV of ~3.27 mm × 3.27 mm with high imaging quality is realized, demonstrating an increased resolving power, four times improvement in FOV and better imaging quality over mobile-optical-polarization imaging device. Importantly, we also demonstrate it has capability of recognizing hemozoin within the sample for malaria diagnosis by imaging malaria-infected blood samples with similar sensitivity comparable to Leica microscopy. It is more compact, portable, and insensitive to alignment, making it highly suitable for malaria detection in a portable, easy to setup and use way in low-resource areas.

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.

Smartphone-based hand-held polarized light microscope for on-site pharmaceutical crystallinity characterization

Polarized light microscopy (PLM) is a common but critical method for pharmaceutical crystallinity characterization, which has been widely introduced for research purposes or drug testing and is recommended by many pharmacopeias around the world. To date, crystallinity characterization of pharmaceutical solids is restricted to laboratories due to the relatively bulky design of the conventional PLM system, while little attention has been paid to on-site, portable, and low-cost applications. Herein, we developed a smartphone-based polarized microscope with an ultra-miniaturization design ("hand-held" scale) for these purposes. The compact system consists of an optical lens, two polarizers, and a tailor-made platform to hold the smartphone. Analytical performance parameters including resolution, imaging quality of interference color, and imaging reproducibility were measured. In a first approach, we illustrated the suitability of the device for pharmaceutical crystallinity characterization and obtained high-quality birefringence images comparable to a conventional PLM system, and we also showed the great promise of the device for on-site characterization with high flexibility. In a second approach, we employed the device as a proof of concept for a wider application ranging from liquid crystal to environmental pollutants or tissues from plants. As such, this smartphone-based hand-held polarized light microscope shows great potential in helping pharmacists both for research purposes and on-site drug testing, not to mention its broad application prospects in many other fields.

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm

Eradication of malaria, a mosquito-borne parasitic disease that hijacks human red blood cells, is a global priority. Microscopy remains the gold standard hallmark for diagnosis and estimation of parasitemia for malaria, to date. However, this approach is time-consuming and requires much expertise especially in malaria-endemic countries or in areas with low-density malaria infection. Thus, there is a need for accurate malaria diagnosis/parasitemia estimation with standardized, fast, and more reliable methods. To this end, we performed a proof-of-concept study using the automated imaging (NanoZoomer) platform to detect the malarial parasite in infected blood. The approach can be used as a steppingstone for malaria diagnosis and parasitemia estimation. Additionally, we created an algorithm (ParasiteMacro) compatible with free online imaging software (ImageJ) that can be used with low magnification objectives (e.g., 5×, 10×, and 20×) both in the NanoZoomer and routine microscope. The novel approach to estimate malarial parasitemia based on modern technologies compared to manual light microscopy demonstrated 100% sensitivity, 87% specificity, a 100% negative predictive value (NPV) and a 93% positive predictive value (PPV). The manual and automated malaria counts showed a good Pearson correlation for low- (R2 = 0.9377, r = 0.9683 and p < 0.0001) as well as high- parasitemia (R2 = 0.8170, r = 0.9044 and p < 0.0001) with low estimation errors. Our robust strategy that identifies and quantifies malaria can play a pivotal role in disease control strategies.

i-scope: A Compact automated fluorescence microscope for cell counting applications in low resource settings

Fluorescence microscopy has widespread applications across biological sciences. It has been routinely used for cell counting, which provides a preliminary diagnostic test for many infectious diseases. Conventional fluorescence microscopes are bulky, expensive, time-intensive and laborious. They often require trained operators to acquire and analyze data. We report a compact automated digital fluorescence microscopy system, i-scope, for cell counting applications. The i-scope employs a total internal reflection fluorescence (TIRF) mode of sample illumination, along with a brightfield mode. It has a magnification of 30X, an optical resolution of ~0.2 µm/pixel and offers sample scanning over 20 mm x 20 mm. A custom-written program enables automated image acquisition and analysis, thereby enhancing ease of operation. It has a compact form-factor and has been developed into a standalone system with a processing unit, screen, and other accessories to offer a portable and economic point-of-care diagnostic solution in low-resource settings. We analysed the performance of the i-scope for milk somatic cell enumeration and benchmarked it against that of a conventional fluorescence microscope.

Foldscope microscope, an inexpensive alternative tool to conventional microscopy-Applications in research and education: A review

Microscope is a device used for the visualization of tiny objects which are not visible to the naked eye. Traditional microscopes have been crucial for the advancement of contemporary science and medicine. Recent advancements in the field of microscopy have fueled its exponential growth rate. However, due to their expensive cost and complicated structure, modern microscopes remain inaccessible to the majority of the public. Nonetheless, the foldscope paper microscope has made it possible for anyone to explore and understand the world of microbes and organisms. In this review, we have listed foldscope-based research projects in various domains, as well as their key properties when compared to traditional research microscopes. In addition, we have briefly explored the impact of a foldscope microscope on public health, clinical diagnostics, forensic science, agriculture, basic science, developmental biology, and education. Moreover, the major drawbacks of paper microscopes and the current steps being taken to upgrade foldscope and its features are discussed in this review. Finally, we have concluded with our perspective that the microscope may be updated to imitate the advancement of a conventional microscope. RESEARCH HIGHLIGHTS: The foldscope, a low-cost instrument for studying the microscopic world. Foldscope applications were compared to conventional microscopes in many sectors. The foldscope microscope's existing limitations and potential prospects are highlighted.

Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.

Democratising “Microscopi”: a 3D printed automated XYZT fluorescence imaging system for teaching, outreach and fieldwork

Commercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing “Microscopi”, an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens.

Microscopic Imaging Methods for Organ-on-a-Chip Platforms

Microscopic imaging is essential and the most popular method for in situ monitoring and evaluating the outcome of various organ-on-a-chip (OOC) platforms, including the number and morphology of mammalian cells, gene expression, protein secretions, etc. This review presents an overview of how various imaging methods can be used to image organ-on-a-chip platforms, including transillumination imaging (including brightfield, phase-contrast, and holographic optofluidic imaging), fluorescence imaging (including confocal fluorescence and light-sheet fluorescence imaging), and smartphone-based imaging (including microscope attachment-based, quantitative phase, and lens-free imaging). While various microscopic imaging methods have been demonstrated for conventional microfluidic devices, a relatively small number of microscopic imaging methods have been demonstrated for OOC platforms. Some methods have rarely been used to image OOCs. Specific requirements for imaging OOCs will be discussed in comparison to the conventional microfluidic devices and future directions will be introduced in this review.

A wide-field microscope utilizing two cellphones for health-care applications

In this report, we report a wide field-of-view (FOV) bright field (BF) microscope with compact and portable optical components, mechanically attached to the existing camera unit of the cellphone. A white screen displayed on a cellphone as the illumination source to pump the sample of interest uniformly for the purpose of the reduction in assembly complexity and alignment. It offers a large FOV of 4.36 × 3.27 mm without digital zoom and a spatial resolution of 1.5 μm. Furthermore, we also have demonstrated the potential application for diseases diagnosis and screening by imaging malaria-infected blood sample and iron deficiency anemia blood sample in resource-constrained settings where mobile phone infrastructure is already ubiquitous but microscope is notoriously scarce.

An affordable, handheld multimodal microscopic system with onboard cell morphology and counting features on a mobile device

A simple yet effective, handheld and flexible bright-field and fluorescence microscopic platform on a smartphone with varying optical magnifications is reported for morphological analysis and onboard cell counting features.

Innovations in Plasmodium spp. diagnosis on diverse detection platforms

In 2019, 229 million cases of malaria were recorded worldwide. For epidemiologic surveillance and proper treatment of persons infected with Plasmodium spp., rapid detection of infections by Plasmodium spp. is critical. Thus, Plasmodium spp. diagnosis is one of the indispensable measures for malaria control. Although microscopy is the gold standard for diagnosis, it has restrictions related mainly to the lack of qualified human resources, which is a problem in many regions. Thus, this review presents major innovations in diagnostic methods as alternatives to or complementary to microscopy. Detection platforms in lateral flow systems, electrochemical immunosensors, molecular biology and, more recently, those integrated with smartphones, are highlighted, among others. The advanced improvement of these tests aims to provide techniques that are sensitive and specific, but also quick, easy to handle and free from the laboratory environment. In this way, the tracking of malaria cases can become increasingly effective and contribute to controlling the disease.

The Future in Sensing Technologies for Malaria Surveillance: A Review of Hemozoin-Based Diagnosis

Early and effective malaria diagnosis is vital to control the disease spread and to prevent the emergence of severe cases and death. Currently, malaria diagnosis relies on optical microscopy and immuno-rapid tests; however, these require a drop of blood, are time-consuming, or are not specific and sensitive enough for reliable detection of low-level parasitaemia. Thus, there is an urge for simpler, prompt, and accurate alternative diagnostic methods. Particularly, hemozoin has been increasingly recognized as an attractive biomarker for malaria detection. As the disease proliferates, parasites digest host hemoglobin, in the process releasing toxic haem that is detoxified into an insoluble crystal, the hemozoin, which accumulates along with infection progression. Given its magnetic, optical, and acoustic unique features, hemozoin has been explored for new label-free diagnostic methods. Thereby, herein, we review the hemozoin-based malaria detection methods and critically discuss their challenges and potential for the development of an ideal diagnostic device.

Recent technical advances in whole slide imaging instrumentation

Microscopic observation of biological specimen smears is the mainstay of diagnostic pathology, as defined by the Digital Pathology Association. Though automated systems for this are commercially available, their bulky size and high cost renders them unusable for remote areas. The research community is investing much effort towards building equivalent but portable, low-cost systems. An overview of such research is presented here, including a comparative analysis of recent reports. This paper also reviews recently reported systems for automated staining and smear formation, including microfluidic devices; and optical and computational automated microscopy systems including smartphone-based devices. Image pre-processing and analysis methods for automated diagnosis are also briefly discussed. It concludes with a set of foreseeable research directions that could lead to affordable, integrated and accurate whole slide imaging systems.

Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots.

Democratising "Microscopi": a 3D printed automated XYZT fluorescence imaging system for teaching, outreach and fieldwork

Commercial fluorescence microscope stands and fully automated XYZt fluorescence imaging systems are generally beyond the limited budgets available for teaching and outreach. We have addressed this problem by developing "Microscopi", an accessible, affordable, DIY automated imaging system that is built from 3D printed and commodity off-the-shelf hardware, including electro-mechanical, computer and optical components. Our design features automated sample navigation and image capture with a simple web-based graphical user interface, accessible with a tablet or other mobile device. The light path can easily be switched between different imaging modalities. The open source Python-based control software allows the hardware to be driven as an integrated imaging system. Furthermore, the microscope is fully customisable, which also enhances its value as a learning tool. Here, we describe the basic design and demonstrate imaging performance for a range of easily sourced specimens.

Cell-phone camera Raman spectrometer

In this report, we describe the design, construction, and operation of a cell-phone-based Raman and emission spectral detector, which when coupled to a diffraction grating and cell-phone camera system provides means for the detection, recording, and identification of chemicals, drugs, and biological molecules, in situ by means of their Raman and fluorescence spectra. The newly constructed cell-phone spectrometer system was used to record Raman spectra from various chemicals and biological molecules including the resonance enhanced Raman spectra of carrots and bacteria. In addition, we present the quantitative analysis of alcohol-water Raman spectra, performed using our cell-phone spectrometer. The designed and constructed system was also used for constructing Raman images of the samples by utilizing a position scanning stage in conjunction with the system. This compact and portable system is well suited for in situ field applications of Raman and fluorescence spectroscopy and may also be an integrated feature of future cell-phones.

Smartphone-based optical spectroscopic platforms for biomedical applications: a review [Invited]

Rapid advancements in smartphone technology have enabled the integration of many optical detection techniques that leverage the embedded functional components and software platform of these sophisticated devices. Over the past few years, several research groups have developed high-resolution smartphone-based optical spectroscopic platforms and demonstrated their usability in different biomedical applications. Such platforms provide unprecedented opportunity to develop point-of-care diagnostics systems, especially for resource-constrained environments. In this review, we discuss the development of smartphone systems for optical spectroscopy and highlight current challenges and potential solutions to improve the scope for their future adaptability.

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Microfluidics in Biotechnology: Quo Vadis

The emerging technique of microfluidics offers new approaches for precisely controlling fluidic conditions on a small scale, while simultaneously facilitating data collection in both high-throughput and quantitative manners. As such, the so-called lab-on-a-chip (LOC) systems have the potential to revolutionize the field of biotechnology. But what needs to happen in order to truly integrate them into routine biotechnological applications? In this chapter, some of the most promising applications of microfluidic technology within the field of biotechnology are surveyed, and a few strategies for overcoming current challenges posed by microfluidic LOC systems are examined. In addition, we also discuss the intensifying trend (across all biotechnology fields) of using point-of-use applications which is being facilitated by new technological achievements.

Polarization-sensitive differential phase-contrast microscopy

We present a novel, to the best of our knowledge, form of polarization microscopy capable of producing quantitative optic-axis and phase retardation maps of transparent and anisotropic materials. The proposed method operates on differential phase-contrast (DPC) microscopy that produces a phase image of a thin specimen using multi-axis intensity measurements. For polarization-sensitive imaging, patterned illumination light is circularly polarized to illuminate a specimen. The light transmitted through a specimen is split into two orthogonal polarization states and measured by an image sensor. Subsequent DPC computation based on the illumination patterns, acquired images, and the imaging model enables the retrieval of polarization-dependent quantitative phase images, which are utilized to reconstruct the orientation and retardation of the specimen. We demonstrate the validity of the proposed method by measuring the optic-axis and phase retardation maps of calibrated and various anisotropic samples.

Lensless, reflection-based dark-field microscopy (RDFM) on a CMOS chip

We present for the first time a lens-free, oblique illumination imaging platform for on-sensor dark- field microscopy and shadow-based 3D object measurements. It consists of an LED point source that illuminates a 5-megapixel, 1.4 µm pixel size, back-illuminated CMOS sensor at angles between 0° and 90°. Analytes (polystyrene beads, microorganisms, and cells) were placed and imaged directly onto the sensor. The spatial resolution of this imaging system is limited by the pixel size (∼1.4 µm) over the whole area of the sensor (3.6×2.73 mm). We demonstrated two imaging modalities: (i) shadow imaging for estimation of 3D object dimensions (on polystyrene beads and microorganisms) when the illumination angle is between 0° and 85°, and (ii) dark-field imaging, at >85° illumination angles. In dark-field mode, a 3-4 times drop in background intensity and contrast reversal similar to traditional dark-field imaging was observed, due to larger reflection intensities at those angles. With this modality, we were able to detect and analyze morphological features of bacteria and single-celled algae clusters.

Point-of-care tests for malaria: speeding up the diagnostics at the bedside and challenges in malaria cases detection

Malaria remains as one of the major public health problems worldwide. About 228 million cases occurred in 2018 only, with Africa bearing about 93% of the cases. Asymptomatic population carrying the various forms of the parasite Plasmodium in endemic areas plays an important role in the spread of the disease. To tackle this battle, more sensitive and precise detection kits for malaria are crucial to better control the number of new malaria cases. In this review, we not only discuss some of the available approaches to rapidly detect new malaria cases in endemic areas but also shed light on parallel problems that may affect the detection of individuals infected with the parasite, covering kelch 13 mutation, glucose 6-phosphate dehydrogenase deficiency, and hemoglobin disorders. Available approaches for malaria detection covered in this review are focused on point-of-care tests, including portable polymerase chain reaction and aptamers.

Nano illumination microscopy: a technique based on scanning with an array of individually addressable nanoLEDs

In lensless microscopy, spatial resolution is usually provided by the pixel density of current digital cameras, which are reaching a hard-to-surpass pixel size / resolution limit over 1 µm. As an alternative, the dependence of the resolving power can be moved from the detector to the light sources, offering a new kind of lensless microscopy setups. The use of continuously scaled-down Light-Emitting Diode (LED) arrays to scan the sample allows resolutions on order of the LED size, giving rise to compact and low-cost microscopes without mechanical scanners or optical accessories. In this paper, we present the operation principle of this new approach to lensless microscopy, with simulations that demonstrate the possibility to use it for super-resolution, as well as a first prototype. This proof-of-concept setup integrates an 8 × 8 array of LEDs, each 5 × 5 μm² pixel size and 10 μm pitch, and an optical detector. We characterize the system using Electron-Beam Lithography (EBL) pattern. Our prototype validates the imaging principle and opens the way to improve resolution by further miniaturizing the light sources.

Cell phone digital microscopy using an oil droplet

We introduce an accessible cell phone imaging method using small droplets of microscope immersion oil and consumer-grade oils. Oil droplets were more resistant to evaporation than water droplets, and they resolved cellular structures that were visible using a 20x/0.75 objective. We optically characterized the droplets using a cell phone screen and resolution target. We further obtained cellular resolution images of an onion epidermis and a zea stem cross-section sample. Our droplet-based method enables stable optical imaging for diagnostic and educational purposes without custom setups, specialized components, or manufacturing processes.

Lidar reveals activity anomaly of malaria vectors during pan-African eclipse

Yearly, a quarter billion people are infected and a half a million killed by the mosquito-borne disease malaria. Lack of real-time observational tools for continuously assessing the unperturbed mosquito flight activity in situ limits progress toward improved vector control. We deployed a high-resolution entomological lidar to monitor a half-kilometer static transect adjacent to a Tanzanian village. We evaluated one-third million insect observations during five nights, four days, and one annular solar eclipse. We demonstrate in situ lidar classification of several insect families and their sexes based on their modulation signatures. We were able to compare the fine-scale spatiotemporal activity patterns of malaria vectors during ordinary days and an eclipse to disentangle phototactic activity patterns from the circadian mechanism. We observed an increased insect activity during the eclipse attributable to mosquitoes. These unprecedented findings demonstrate how lidar-based monitoring of distinct mosquito activities could advance our understanding of vector ecology.

Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications

Portable, low-cost smartphone platform microscopic systems have emerged as a potential tool for imaging of various micron and submicron scale particles in recent years (Ozcan; Pirnstill and Coté; Breslauer et al.; Zhu et al.). In most of the reported works, it involves either the use of sophisticated optical set-ups along with a high-end computational tool for postprocessing of the captured images, or it requires a high-end configured smartphone to obtain enhanced imaging of the sample. Present work reports the working of a low-cost, field-portable 520× optical microscope using a smartphone. The proposed smartphone microscopic system has been designed by attaching a 3D printed compact optical set-up to the rear camera of a regular smartphone. By using cloud-based services, an image processing algorithm has been developed which can be accessed anytime through a mobile broadband network. Using this facility, the quality of the captured images can be further enhanced, thus obviating the need for dedicated computational tools for postprocessing of the images. With the designed microscopic system, an optical resolution ∼2 µm has been obtained. Upon postprocessing, the resolution of the captured images can be improved further. It is envisioned that with properly designed optical set-up in 3D printer and by developing an image processing application in the cloud, it is possible to obtain a low-cost, user-friendly, field-portable optical microscope on a regular smartphone that performs at par with that of a laboratory-grade microscope. LAY DESCRIPTION: With the ever-improving features both in hardware and software part, smartphone becomes ubiquitous in the modern civilised society with approximately 8.1 billion cell phone users across the world, and ∼40% of them can be considered as smartphones. This technology is undoubtedly the leading technology of the 21st century. Very recently, various researchers across the globe have utilised different sensing components embedded in the smartphone to convert it into a field-portable low-cost and user-friendly tool which can be used for different sensing and imaging purposes. By using simple optical components such as lens, pinhole, diffuser etc. and the camera of the smartphone, various groups have converted the phone into a microscopic imaging system. Again, by removing the camera lenses of the phone, holography images of microscopic particles by directly casting its shadows on the CMOS sensor on the phone has been demonstrated. The holographic images have subsequently been processed using the dedicated computational tool, and the original photos of the samples can be obtained. All the reported smartphone-based microscopic systems either suffer from relatively low field-of-view (FOV), resolution or it needs a high computational platform. Present work, demonstrate an alternative approach by which a reasonably good resolution (<2 µm) along with high optical magnification (520×) and a large FOV (150 µm) has been obtained on a regular smartphone. For postprocessing of the captured images an image processing algorithm has been developed in the cloud and the same can be accessed by the smartphone application, obviating the need of dedicated computational tool and a high-end configured smartphone for the proposed microscope. For the development of the proposed microscopic system, a simple optical set-up has been fabricated in a 3D printer. The set-up houses all the required optical components and the sample specimen with the 3D-printed XY stage, and it can be attached easily to the rear camera of the smartphone. Using the proposed microscopic system, enhanced imaging of USAF target and red blood cells have been successfully demonstrated. With the readily available optical components and a regular smartphone, the net cost involvement is significantly low (less than $250, including the smartphone). We envisioned that the designed system could be utilised for point-of-care diagnosis in resource-poor settings where access to the laboratory facilities is very limited.

Portable bright-field, fluorescence, and cross-polarized microscope toward point-of-care imaging diagnostics

Emerging technologies are enabling the feasibility of new types of point-of-care diagnostic devices. A portable, multimodal microscopy platform intended for use in remote diagnostic applications is presented. Use of such a system could bring high-quality microscopy to field use for diseases such as malaria, allowing better diagnostic and surveillance information to be gathered. The microscope was designed using off-the-shelf components and a manual filter selection to generate bright-field, fluorescent, and cross-polarized images of samples mounted to microscopy slides. Design parameters for the system are discussed, and characterization is performed using standardized imaging targets, multimodal phantoms, and blood smears simulating those used in malaria diagnosis. The microscope is shown to be able to image below element 9-3 of a 1951 U.S. Air Force target, indicating that the system is capable of resolving features < 775  nm. Morphological indicators of Plasmodium falciparum can be visualized in images from each modality and combined into high-contrast composite images. To optimize parasitic feature contrast across all three imaging modes, several different staining techniques were compared, with results indicating that use of a single nucleic acid binding fluorophore is preferable.

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-world use. Here we propose a cost-effective solution: the augmented reality microscope (ARM). The ARM overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows. We demonstrate the utility of ARM in the detection of metastatic breast cancer and the identification of prostate cancer, with latency compatible with real-time use. We anticipate that the ARM will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.

Nuclear morphometrics and chromatin condensation patterns as disease biomarkers using a mobile microscope

Current cancer diagnosis involves the use of nuclear morphology and chromatin condensation signatures for accurate advanced stage classification. While such diagnostic approaches rely on high resolution imaging of the cell nucleus using expensive microscopy systems, developing portable mobile microscopes to visualize nuclear and chromatin condensation patterns is desirable at clinical settings with limited infrastructure. In this study, we develop a portable fluorescent mobile microscope capable of acquiring high resolution images of the nucleus and chromatin. Using this we extracted nuclear morphometric and chromatin texture based features and were able to discriminate between normal and cancer cells with similar accuracy as wide-field fluorescence microscopy. We were also able to detect subtle changes in nuclear and chromatin features in cells subjected to compressive forces, cytoskeletal perturbations and cytokine stimulation, thereby highlighting the sensitivity of the portable microscope. Taken together, we present a versatile platform to exploit nuclear morphometrics and chromatin condensation features as physical biomarkers for point-of-care diagnostic solutions.

Detection of breast cancer lymph node metastases in frozen sections with a point-of-care low-cost microscope scanner

Background

Detection of lymph node metastases is essential in breast cancer diagnostics and staging, affecting treatment and prognosis. Intraoperative microscopy analysis of sentinel lymph node frozen sections is standard for detection of axillary metastases but requires access to a pathologist for sample analysis. Remote analysis of digitized samples is an alternative solution but is limited by the requirement for high-end slide scanning equipment.

Objective

To determine whether the image quality achievable with a low-cost, miniature digital microscope scanner is sufficient for detection of metastases in breast cancer lymph node frozen sections.

Methods

Lymph node frozen sections from 79 breast cancer patients were digitized using a prototype miniature microscope scanner and a high-end slide scanner. Images were independently reviewed by two pathologists and results compared between devices with conventional light microscopy analysis as ground truth.

Results

Detection of metastases in the images acquired with the miniature scanner yielded an overall sensitivity of 91% and specificity of 99% and showed strong agreement when compared to light microscopy (k = 0.91). Strong agreement was also observed when results were compared to results from the high-end slide scanner (k = 0.94). A majority of discrepant cases were micrometastases and sections of which no anticytokeratin staining was available.

Conclusion

Accuracy of detection of metastatic cells in breast cancer sentinel lymph node frozen sections by visual analysis of samples digitized using low-cost, point-of-care microscopy is comparable to analysis of digital samples scanned using a high-end, whole slide scanner. This technique could potentially provide a workflow for digital diagnostics in resource-limited settings, facilitate sample analysis at the point-of-care and reduce the need for trained experts on-site during surgical procedures.

Dual-phone illumination-imaging system for high resolution and large field of view multi-modal microscopy

In this paper we present for the first time a system comprised of two mobile phones, one for illumination and the other for microscopy, as a portable, user-friendly, and cost-effective microscopy platform for a wide range of applications. Versatile and adaptive illumination is made with a Retina display of an Apple mobile phone device. The phone screen is used to project various illumination patterns onto the specimen being imaged, each corresponding to a different illumination mode, such as bright-field, dark-field, point illumination, Rheinberg illumination, and fluorescence microscopy. The second phone (a Nokia phone) is modified to record microscopic images about the sample. This imaging platform provides a high spatial resolution of at least 2 μm, a large field-of-view of 3.6 × 2.7 mm, and a working distance of 0.6 mm. We demonstrate the performance of this platform for the visualization of microorganisms within microfluidic devices to gather qualitative and quantitative information regarding microorganism morphology, dimension, count, and velocity/trajectories in the x-y plane.

Accurate 3D Shape, Displacement and Deformation Measurement Using a Smartphone

The stereo-digital image correlation technique using two synchronized industrial-grade cameras has been extensively used for full-field 3D shape, displacement and deformation measurements. However, its use in resource-limited institutions and field settings is inhibited by the need for relatively expensive, bulky and complicated experimental set-ups. To mitigate this problem, we established a cost-effective and ultra-portable smartphone-based stereo-digital image correlation system, which only uses a smartphone and an optical attachment. This optical attachment is composed of four planar mirrors and a 3D-printed mirror support, and can split the incoming scene into two sub-images, simulating a stereovision system using two virtual smartphones. Although such a mirror-based system has already been used for stereo-image correlation, this is the first time it has been combined with a commercial smartphone. This publication explores the potential and limitations of such a configuration. We first verified the effectiveness and accuracy of this system in 3D shape and displacement measurement through shape measurement and in-plane and out-of-plane translation tests. Severe thermal-induced virtual strains (up to 15,000 με) were found in the measured results due to the smartphone heating. The mechanism for the generation of the temperature-dependent errors in this system was clearly and reasonably explained. After a simple preheating process, the smartphone-based system was demonstrated to be accurate in measuring the strain on the surface of a loaded composite specimen, with comparable accuracy to a strain gauge. Measurements of 3D deformation are illustrated by tracking the deformation on the surface of a deflating ball. This cost-effective and ultra-portable smartphone-based system not only greatly decreases the hardware investment in the system construction, but also increases convenience and efficiency of 3D deformation measurements, thus demonstrating a large potential in resource-limited and field settings.

Malaria Detection by Third-Harmonic Generation Image Scanning Cytometry

Despite global efforts aimed at its elimination, malaria is still a significant health concern in many countries across the world. The disease is caused by blood-borne parasites, Plasmodium species, and is transmitted by female Anopheles mosquitoes and presents with generic febrile symptoms that are challenging to diagnose clinically. To adequately tackle this issue, an effective detection method is required for screening potential malaria patients for infection. To this day, the gold standard for malaria detection remains basic light microscopy of Giemsa-stained patient blood smears to first enable detection and manual counting to determine the parasite density by a microscopist. While effective at detecting parasites, this method requires both significant time and skilled personnel. As an alternate approach, we propose a new malaria detection method that we call third-harmonic generation image scanning cytometry (THGISC) based on the combination of third-harmonic generation imaging, high-speed motorized scanning, and automated software processing. Third-harmonic generation (THG) is a nonlinear optical process in which the frequency of incident photons is tripled within the sample material. We have previously demonstrated that hemozoin, a metabolic byproduct of the malaria parasite, presents a significant THG signal. We now present a practical approach that uses the selectivity of this contrast mechanism to perform label-free image scanning cytometry of patient blood smears for automated malaria detection. In this work, we applied this technique to lab-cultured parasites and parasites in whole blood obtained from malaria patients. We also compared its effectiveness to parasite counts obtained by classical methods. The ability to easily and rapidly determine parasitemia by THG offers potential not only for the easy confirmation of malaria diagnoses following symptoms, but also the tracking of treatment progress in existing patients, potentially allowing physicians to adjust medication and dosage for each individual.

Imaging & identification of malaria parasites using cellphone microscope with a ball lens

We have optimized the design and imaging procedures, to clearly resolve the malaria parasite in Giemsa-stained thin blood smears, using simple low-cost cellphone-based microscopy with oil immersion. The microscope uses a glass ball as the objective and the phone camera as the tube lens. Our optimization includes the optimal choice of the ball lens diameter, the size and the position of the aperture diaphragm, and proper application of immersion, to achieve diagnostic capacity in a wide field of view. The resulting system is potentially applicable to low-cost in-the-field optical diagnostics of malaria as it clearly resolves micron-sized features and allows for analysis of parasite morphology in the field of 50 × 50 μm, and parasite detection in the field of at least 150 × 150 μm.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.

Design of a 3D printed compact interferometric system and required phone application for small angular measurements

A 3D printed smartphone based interferometric system is proposed, and its usability has been demonstrated by measuring small angular rotations. All necessary fringe processing and data analysis have been performed within the phone itself using custom designed application developed in an android platform. The main objective of the proposed work is to demonstrate the usability of modern smartphone and 3D printing technology for optical interferometric applications. The smartphone camera has been used to record the interference fringes which has been formed due to the change in the optical path difference (OPD) between light rays reflected from the top and bottom surface of a microscopic glass slide. The angular variation of the slide causes a detectable change in the OPD between the interfering beams which subsequently would cause a variation in the fringe pattern. By evaluating necessary interferometric parameters, small angular rotation can be computed within the smartphone application. With the designed smartphone based interferometric system, angular rotation as small as 0.02° can be measured accurately and reliably having a dynamic range of -3.68° to 3.68°. Due to the involvement of the smartphone as a platform for recording as well as onboard fringe processing, the designed interferometric system can be visualized as a truly field portable tool for different optical metrological applications.

The Promise of Mobile Technologies for the Health Care System in the Developing World: A Systematic Review

Evolution of mobile technologies and their rapid penetration into people's daily lives, especially in the developing countries, have highlighted mobile health, or m-health, as a promising solution to improve health outcomes. Several studies have been conducted that characterize the impact of m-health solutions in resource-limited settings and assess their potential to improve health care. The aim of this review is twofold: 1) to present an overview of the background and significance of m-health and 2) to summarize and discuss the existing evidence for the effectiveness of m-health in the developing world. A systematic search in the literature was performed in Pubmed, Scopus, as well as reference lists, and a broad sample of 98 relevant articles was identified, which were then categorized into five wider m-health categories. Although statistically significant conclusions cannot be drawn since the majority of studies relied on small-scale trials and limited assessment of long-term effects, this review provides a systematic and extensive analysis of the advantages, disadvantages, and challenges of m-health in developing countries in an attempt to determine future research directions of m-health interventions.

Mobile-phone-based Rheinberg microscope with a light-emitting diode array

Mobile phone technology has led to implementation of portable and inexpensive microscopes. Light-emitting diode (LED) array microscopes support various multicontrast imaging by flexible illumination patterns of the LED array that can be achieved without changing the optical components of the microscope. Here, we demonstrate a mobile-phone-based LED array microscope to realize multimodal imaging with bright-field, dark-field, differential phase-contrast, and Rheinberg illuminations using as few as 37 LED bulbs. Using this microscope, we obtained high-contrast images of living cells. Furthermore, by changing the color combinations of Rheinberg illumination, we were able to obtain images of living chromatic structures with enhanced or diminished contrast. This technique is expected to be a foundation for high-contrast microscopy used in modern field studies.

Liquid crystal based sensing device using a smartphone

Liquid crystal (LC) based optical sensors have been found to be very promising for detecting aqueous biological samples due to the ease of optical detection, their cost effectiveness and the removal of the need for labelling biological species with fluorescent dyes. To date, all LC based sensors are studied in laboratories using conventional polarizing optical microscopy (POM), and no attention has been paid towards the fabrication of portable LC sensing devices for use in commercial purposes. Here, we designed and fabricated a 3D printed portable, lightweight, and inexpensive sensing device using a smartphone to detect the optical signal of LC based sensors. The accuracy of the optical signal using the fabricated sensing device is similar to that obtained using conventional POM. The fabricated sensing device, using a smartphone, gives a novel and new platform to LC based sensors for practical applications in the industrial world and people's daily lives.

Malaria and the 'last' parasite: how can technology help?

Malaria, together with HIV/AIDS, tuberculosis and hepatitis are the four most deadly infectious diseases globally. Progress in eliminating malaria has saved millions of lives, but also creates new challenges in detecting the 'last parasite'. Effective and accurate detection of malaria infections, both in symptomatic and asymptomatic individuals are needed. In this review, the current progress in developing new diagnostic tools to fight malaria is presented. An ideal rapid test for malaria elimination is envisioned with examples to demonstrate how innovative technologies can assist the global defeat against this disease. Diagnostic gaps where technology can bring an impact to the elimination campaign for malaria are identified. Finally, how a combination of microfluidic-based technologies and smartphone-based read-outs could potentially represent the next generation of rapid diagnostic tests is discussed.