Quantitative imaging with a mobile phone microscope

Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices

We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.

A commentary on the development and use of smartphone imaging devices

Current-age smartphones are known for their wide array of functionality and are now being utilized in the field of healthcare and medicine due to their proven capabilities as smartphone imaging devices (SIDs). Recent technical advancements enabled the integration of special add-on lenses with smartphones to transform them into SIDs. With the rising demand for efficient point-of-care (PoC) devices for better diagnostic applications, SIDs will be a one-stop solution. Additionally, portability, user-friendliness and low-cost make it accessible for all even at remote locations. Furthermore, improvements in resolution, magnification and field-of-view (FOV) have attracted the scientific community to use SIDs in various biomedical applications such as disease diagnosis, food quality control and pathogen detection. SIDs can be arranged in various combinational setups by using different illumination sources and optics to achieve suitable contrast and visibility of the specimen under study. This Commentary illustrates the various illumination sources used in SID and also spotlights their design and applications.

An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water

Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.

Large depth-of-field ultra-compact microscope by progressive optimization and deep learning

The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm³ and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditional microscopes with great generalization in a wide variety of samples. To show the unique advantages, the integrated microscope is equipped in a cell phone without any accessories for the application of portable diagnostics. We believe our method provides a new framework for the design of miniaturized high-performance imaging systems by integrating aspherical optics, computational optics, and deep learning.

Thyroid Cytopathology Cancer Diagnosis from Smartphone Images Using Machine Learning

We examined the performance of deep learning models on the classification of thyroid fine-needle aspiration biopsies using microscope images captured in 2 ways: with a high-resolution scanner and with a mobile phone camera. Our training set consisted of images from 964 whole-slide images captured with a high-resolution scanner. Our test set consisted of 100 slides; 20 manually selected regions of interest (ROIs) from each slide were captured in 2 ways as mentioned above. Applying a baseline machine learning algorithm trained on scanner ROIs resulted in performance deterioration when applied to the smartphone ROIs (97.8% area under the receiver operating characteristic curve [AUC], CI = [95.4%, 100.0%] for scanner images vs 89.5% AUC, CI = [82.3%, 96.6%] for mobile images, P = .019). Preliminary analysis via histogram matching showed that the baseline model was overly sensitive to slight color variations in the images (specifically, to color differences between mobile and scanner images). Adding color augmentation during training reduces this sensitivity and narrows the performance gap between mobile and scanner images (97.6% AUC, CI = [95.0%, 100.0%] for scanner images vs 96.0% AUC, CI = [91.8%, 100.0%] for mobile images, P = .309), with both modalities on par with human pathologist performance (95.6% AUC, CI = [91.6%, 99.5%]) for malignancy prediction (P = .398 for pathologist vs scanner and P = .875 for pathologist vs mobile). For indeterminate cases (pathologist-assigned Bethesda category of 3, 4, or 5), color augmentations confer some improvement (88.3% AUC, CI = [73.7%, 100.0%] for the baseline model vs 96.2% AUC, CI = [90.9%, 100.0%] with color augmentations, P = .158). In addition, we found that our model's performance levels off after 15 ROIs, a promising indication that ROI data collection would not be time-consuming for our diagnostic system. Finally, we showed that the model has sensible Bethesda category (TBS) predictions (increasing risk malignancy rate with predicted TBS category, with 0% malignancy for predicted TBS 2 and 100% malignancy for TBS 6).

Fully integrated point-of-care blood cell count using multi-frame morphology analysis

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

A review on low-cost microscopes for Open Science

This article presents a review after an exhaustive search that yielded 23 works carried out in the last decade for the availability of optical microscopes with open hardware as a low-cost alternative to commercial systems. These works were developed with the aim of covering needs within several areas such as: Bio Sciences research in institutions with limited resources, diagnosis of diseases and health screenings in large populations in developing countries, and training in educational contexts with a need for high availability of equipment and low replacement cost. The analysis of the selected works allows us to classify the analyzed solutions into two main categories, for which their essential characteristics are enumerated: portable field microscopes and multipurpose automated microscopes. Moreover, this work includes a discussion on the degree of maturity of the solutions in terms of the adoption of practices aligned with the development of Open Science. RESEARCH HIGHLIGHTS: Concise review on low-cost microscopes for developing Open Science, exposing the role of smartphone-based microscopy. The work classifies microscopes in two main categories: (1) portable field microscopes, and (2) multipurpose automated microscopes.

Challenges and Opportunities in Using Twitter as a Pedagogical Tool in Pathology and Laboratory Medicine Education

OBJECTIVES

Even though the benefits of using Twitter for teaching and information exchange within the pathology community are primarily undisputed, a fair number of users refrain from sharing educational content on this social media platform. The survey aims to identify challenges in using Twitter as a pedagogical tool and discuss ways of converting those challenges into opportunities.

METHODS

We performed a cross-sectional survey-based study involving 174 participants from the pathology community on Twitter.

RESULTS

The results demonstrated that procurement of content was the major challenge faced by 36.2% of the participants, with most participants being concerned or very concerned regarding institutional policies and Health Insurance Portability and Accountability Act issues. Content procurement concerns were followed by social media inertia, stated by 23% of the participants as an obstacle to posting educational content on Twitter.

CONCLUSIONS

The report helps elicit some common obstacles faced by the pathology and laboratory medicine community and allows us to recommend solutions to overcome these obstacles.

Examination of blood samples using deep learning and mobile microscopy

BACKGROUND

Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.

RESULTS

Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected.

CONCLUSIONS

Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care.

Wound Size Imaging: Ready for Smart Assessment and Monitoring

Significance: We introduce and evaluate emerging devices and modalities for wound size imaging and also promising image processing tools for smart wound assessment and monitoring. Recent Advances: Some commercial devices are available for optical wound assessment but with limited possibilities compared to the power of multimodal imaging. With new low-cost devices and machine learning, wound assessment has become more robust and accurate. Wound size imaging not only provides area and volume but also the proportion of each tissue on the wound bed. Near-infrared and thermal spectral bands also enhance the classical visual assessment. Critical Issues: The ability to embed advanced imaging technology in portable devices such as smartphones and tablets with tissue analysis software tools will significantly improve wound care. As wound care and measurement are performed by nurses, the equipment needs to remain user-friendly, enable quick measurements, provide advanced monitoring, and be connected to the patient data management system. Future Directions: Combining several image modalities and machine learning, optical wound assessment will be smart enough to enable real wound monitoring, to provide clinicians with relevant indications to adapt the treatments and to improve healing rates and speed. Sharing the wound care histories of a number of patients on databases and through telemedicine practice could induce a better knowledge of the healing process and thus a better efficiency when the recorded clinical experience has been converted into knowledge through deep learning.

On the diffraction of a high-NA aplanatic and stigmatic singlet

We present a study of the diffraction pattern according to Richards-Wolf for an aplanatic and stigmatic singlet based on an exact analytical equation. We are able to put emphasis on the maximum diameter and illumination pattern, which are the two parameters that influence the diffraction pattern and how to compute it. Designs of low- and high-NA aplanatic and stigmatic lenses are implemented to display these effects.

PhaseRMiC: phase real-time microscope camera for live cell imaging

We design a novel phase real-time microscope camera (PhaseRMiC) for live cell phase imaging. PhaseRMiC has a simple and cost-effective configuration only consisting of a beam splitter and a board-level camera with two CMOS imaging chips. Moreover, integrated with 3-D printed structures, PhaseRMiC has a compact size of 136×91×60 mm³, comparable to many commercial microscope cameras, and can be directly connected to the microscope side port. Additionally, PhaseRMiC can be well adopted in real-time phase imaging proved with satisfied accuracy, good stability and large field of view. Considering its compact and cost-effective device design as well as real-time phase imaging capability, PhaseRMiC is a preferred solution for live cell imaging.

3D printed imaging platform for portable cell counting

Despite having widespread application in the biomedical sciences, flow cytometers have several limitations that prevent their application to point-of-care (POC) diagnostics in resource-limited environments. 3D printing provides a cost-effective approach to improve the accessibility of POC devices in resource-limited environments. Towards this goal, we introduce a 3D-printed imaging platform (3DPIP) capable of accurately counting particles and perform fluorescence microscopy. In our 3DPIP, captured microscopic images of particle flow are processed on a custom developed particle counter code to provide a particle count. This prototype uses a machine vision-based algorithm to identify particles from captured flow images and is flexible enough to allow for labeled and label-free particle counting. Additionally, the particle counter code returns particle coordinates with respect to time which can further be used to perform particle image velocimetry. These results can help estimate forces acting on particles, and identify and sort different types of cells/particles. We evaluated the performance of this prototype by counting 10 μm polystyrene particles diluted in deionized water at different concentrations and comparing the results with a commercial Beckman-Coulter Z2 particle counter. The 3DPIP can count particle concentrations down to ∼100 particles per mL with a standard deviation of ±20 particles, which is comparable to the results obtained on a commercial particle counter. Our platform produces accurate results at flow rates up to 9 mL h-1 for concentrations below 1000 particle per mL, while 5 mL h-1 produces accurate results above this concentration limit. Aside from performing flow-through experiments, our instrument is capable of performing static experiments that are comparable to a plate reader. In this configuration, our instrument is able to count between 10 and 250 cells per image, depending on the prepared concentration of bacteria samples (Citrobacter freundii; ATCC 8090). Overall, this platform represents a first step towards the development of an affordable fully 3D printable imaging flow cytometry instrument for use in resource-limited clinical environments.

Smartphone-based optical analysis systems

During the past few decades, there has been a growing trend towards the use of smartphone-based analysis systems. This is mainly due to its ubiquity, its increasing computing capacity, its relatively low cost and the ability to acquire and process data at the same time. Furthermore, there are many sensors integrated into a smartphone, for example a complementary metal-oxide semiconductor (CMOS) sensor. A CMOS sensor enables optical analysis for example by using it as a colorimeter, photometer or spectrometer. This review explores the current state-of-the-art smartphone-based optical analysis systems in various areas of application. It is organized into three sections, each of which investigates one class of smartphone-based devices: (i) smartphone-based colorimeters (ii) smartphone-based photo- and spectrometers and (iii) smartphone-based fluorimeters.

Design of a Cell Phone Lens-Based Miniature Microscope with Configurable Magnification Ratio

Application of cell-phone-based microscopes has been hindered by limitations such as inferior image quality, fixed magnification and inconvenient operation. In this paper, we propose a reverse cell phone lens-based miniature microscope with a configurable magnification ratio. By switching the objectives of three camera lens and applying the digital zooming function of the cell phone, a cell phone microscope is built with the continuously configurable magnification ratio between 0.8×–11.5×. At the same time, the miniature microscope can capture high-quality microscopic images with a maximum resolution of up to 575 lp/mm and a maximum field of view (FOV) of up to 7213 × 5443 μm. Furthermore, by moving the tube lens module of the microscope out of the cell phone body, the built miniature microscope is as compact as a <20 mm side length cube, improving operational experience profoundly. The proposed scheme marks a big step forward in terms of the imaging performance and user operational convenience for cell phone microscopes.

Smartphone-based imaging systems for medical applications: a critical review

SIGNIFICANCE

Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed.

AIM

Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances.

APPROACH

First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed.

RESULTS

The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo, multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications.

CONCLUSIONS

A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forward.

Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry

High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.

Pocket MUSE: an affordable, versatile and high-performance fluorescence microscope using a smartphone

Smartphone microscopes can be useful tools for a broad range of imaging applications. This manuscript demonstrates the first practical implementation of Microscopy with Ultraviolet Surface Excitation (MUSE) in a compact smartphone microscope called Pocket MUSE, resulting in a remarkably effective design. Fabricated with parts from consumer electronics that are readily available at low cost, the small optical module attaches directly over the rear lens in a smartphone. It enables high-quality multichannel fluorescence microscopy with submicron resolution over a 10× equivalent field of view. In addition to the novel optical configuration, Pocket MUSE is compatible with a series of simple, portable, and user-friendly sample preparation strategies that can be directly implemented for various microscopy applications for point-of-care diagnostics, at-home health monitoring, plant biology, STEM education, environmental studies, etc.

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2

Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts.

Detection of Bacterial and Viral Pathogens Using Photonic Point-of-Care Devices

Infectious diseases caused by bacteria and viruses are highly contagious and can easily be transmitted via air, water, body fluids, etc. Throughout human civilization, there have been several pandemic outbreaks, such as the Plague, Spanish Flu, Swine-Flu, and, recently, COVID-19, amongst many others. Early diagnosis not only increases the chance of quick recovery but also helps prevent the spread of infections. Conventional diagnostic techniques can provide reliable results but have several drawbacks, including costly devices, lengthy wait time, and requirement of trained professionals to operate the devices, making them inaccessible in low-resource settings. Thus, a significant effort has been directed towards point-of-care (POC) devices that enable rapid diagnosis of bacterial and viral infections. A majority of the POC devices are based on plasmonics and/or microfluidics-based platforms integrated with mobile readers and imaging systems. These techniques have been shown to provide rapid, sensitive detection of pathogens. The advantages of POC devices include low-cost, rapid results, and portability, which enables on-site testing anywhere across the globe. Here we aim to review the recent advances in novel POC technologies in detecting bacteria and viruses that led to a breakthrough in the modern healthcare industry.

Application of Texture and Fractal Dimension Analysis to Estimate Effectiveness of Oral Leukoplakia Treatment Using an Er:YAG Laser-A Prospective Study

BACKGROUND

Oral leukoplakia (OL) is a potential neoplasmic lesion. The aim of this study was to apply texture analysis (TA) and fractal dimension analysis (FDA) to estimate the effectiveness of OL treatment using an Er:YAG laser.

METHODS

Eighteen patients with 32 lesions were treated. Laser procedures were conducted using the LiteTouch™ Er:YAG Dental Laser. The diameter of the operational tip was 1.3 mm, the power was 50 mJ, the frequency was 50 Hz, and the wavelength was 2940 nm. TA was based on long and short-run emphasis inverse moments, difference entropy, inverse difference moment, and wavelet decomposition for two-dimensional photography. FDA was measured using the box-counting method.

RESULTS

Total response was achieved in 50% of lesions, partial response was observed in 47%, and 3% of lesions did not respond to treatment. Recurrence occurred in 34% of lesions. TA features indicated pathological images depicting leukoplakia and complete reconstruction of the correct mucosal image after laser ablation. The discrete wavelet transformation feature detects much larger structures than the properties derived from the run-length matrix and co-occurrence matrix.

CONCLUSIONS

The Er:YAG laser is an effective treatment method in cases of oral leukoplakia. Leukoplakia treatment by Er:YAG laser is an effective modality, as revealed by the oral mucosa microstructure. TA and FDA are promising methods to estimate the effectiveness of OL treatment.

Smartphone-based optical assays in the food safety field

Smartphone based devices (SBDs) have the potential to revolutionize food safety control by empowering citizens to perform screening tests. To achieve this, it is of paramount importance to understand current research efforts and identify key technology gaps. Therefore, a systematic review of optical SBDs in the food safety sector was performed. An overview of reviewed SBDs is given focusing on performance characteristics as well as image analysis procedures. The state-of-the-art on commercially available SBDs is also provided. This analysis revealed several important technology gaps, the most prominent of which are: (i) the need to reach a consensus regarding optimal image analysis, (ii) the need to assess the effect of measurement variation caused by using different smartphones and (iii) the need to standardize validation procedures to obtain robust data. Addressing these issues will drive the development of SBDs and potentially unlock their massive potential for citizen-based food control.

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Optical smartphone based sensors in the food safety field are systematically reviewed.

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Recommendations on image analysis optimization are given.

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The analytical performance of smartphone based sensors is discussed.

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Available commercial devises are critically compared.

Development and characterization of portable smartphone-based imaging device

Many diseases in rural areas and developing countries are detected late at an advanced stage when treatment might involve complications and higher cost, resulting in a greater number of fatalities. This study aims to make early disease detection simpler and affordable for people living in remote areas and developing countries. A new age optical microscope with high sensitivity diagnosis can revolutionize this gap in disease detection. Here, a smartphone-based imaging device (SID) using optics and a smartphone interface was developed to speedup the process of diagnosis in areas that do not have easy access to health centers and diagnostic clinics. The device was built using acrylic sheets to make it less bulky and customizable and three-dimensional (3D) printed mechanical parts were used to increase stability. The study includes calibration, and testing the device with various samples to determine its capabilities. Images were acquired using the various types of BLIPS lens integrated onto the smartphone camera lens and compared with optical microscope images. The device can visualize single human blood cell which is 8 μm in size using ultra-BLIPS lens and magnification is comparable to an objective lens used in an optical microscope.

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.

Foldscope as a primary diagnostic tool for oral and urinary tract infections and its effectiveness in oral health education

Due to its speed, accuracy and cost-effectiveness, microscopy has become an integral part of clinical examination for disease diagnosis. However, modern microscopes are very costly and require skilled personnel for their operation and maintenance, and specimen processing and analysis is labour-intensive. Further, lack of such expensive diagnostic tools in remote areas is a serious concern. Affordable point-of-care diagnostic tools are the most useful for timely disease diagnosis and management. The Foldscope is an affordable origami-based microscopy device composed of a series of paper clippings, which, upon assembly, can hold a specimen slide for observation, and this specimen can be viewed via a mobile phone camera attached to it. The present study evaluated the use of the Foldscope in the clinical diagnosis of oral and urinary tract infections and evaluated its efficacy as a motivational tool for improving oral health among school children in India. We qualitatively compared the Foldscope to a clinical microscope by examining five different types of clinical samples. Of the different types of clinical samples, the Foldscope was effective in detecting infection in dental plaque samples and urine samples. Thus, we further analysed 31 dental plaque samples of patients aged 3-13 years and 25 urine samples of patients aged 11-62 years. We also evaluated the use of the Foldscope as an educational tool for motivating oral hygiene among 80 school children aged 12 years and found that students in the Foldscope intervention group had better measures of oral hygiene than did students in the nonintervention group. In summary, our study indicated that the Foldscope is useful in detecting urinary tract infections and kidney stones in urine samples and is a useful motivational tool for oral health education among school-aged children. Furthermore, it may also be useful in oral health monitoring in resource poor settings. LAY DESCRIPTION: Poor and remote population often suffers due to lack of capacity to buy products or avail services which are expensive. In such a society people are not able to afford even the basic detection of diseases. Foldscope may come to rescue here! Foldscope is a paper-based, use-and-throw, affordable microscope. This study explores the use of Foldscope in clinics and diseases diagnosis using patient samples. Preliminary detection of disease associated symptoms in dental and urinary infections and digital record keeping via storage in mobile phone memory and hospital OPD records for monitoring patient's health are some of the advantages of Foldscope for clinical use in resource poor settings. It identifies that Foldscope not only can be used by students or teachers for educational purposes but it can also pave a path for promotion of dental hygiene among young children. The study also suggests that further improvement in design or resolution of Foldscope will broaden the scope of its application, in future.

Differential interference contrast microscopy with adjustable plastic Sanderson prisms

Differential interference contrast (DIC) microscopy is a technique to image spatially dependent gradients in optical path lengths. Contrast is produced through the splitting of polarized light with quartz Wollaston prisms. Here we demonstrate that light splitting for DIC microscopy can also be achieved with Sanderson prisms consisting of polycarbonate bars under a bending load. Comparable image contrast while imaging cultured cells was achieved with this alternative technique. These results demonstrate an inexpensive and easily adjustable alternative to traditional quartz Wollaston prisms.

Visualization of blood cell contrast in nailfold capillaries with high-speed reverse lens mobile phone microscopy

Quantification of optical absorption gaps in nailfold capillaries has recently shown promise as a non-invasive technique for neutropenia screening. Here we demonstrate a low-cost, portable attachment to a mobile phone that can resolve optical absorption gaps in nailfold capillaries using a reverse lens technique and oblique 520nm illumination. Resolution <4μm within a 1mm2 on-axis region is demonstrated, and wide field of view (3.5mm × 4.8mm) imaging is achieved with resolution <6μm in the periphery. Optical absorption gaps (OAGs) are visible in superficial capillary loops of a healthy human participant by an ∼8-fold difference in contrast-to-noise ratio with respect to red blood cell absorption contrast. High speed video capillaroscopy up to 240 frames per second (fps) is possible, though 60fps is sufficient to resolve an average frequency of 37 OAGs/minute passing through nailfold capillaries. The simplicity and portability of this technique may enable the development of an effective non-invasive tool for white blood cell screening in point-of-care and global health settings.

Improving the limit of detection in portable luminescent assay readers through smart optical design

Critical biomarkers of disease are increasingly being detected by point-of-care assays. Chemiluminescence (CL) and electrochemiluminescence (ECL) are often used in such assays due to their convenience and that they do not require light sources or other components that could complicate or add cost to the system. Reports of these assays often include readers built on a cellphone platform or constructed from low-cost components. However, the impact the optical design has on the limit of detection (LOD) in these systems remains unexamined. Here, we report a theoretical rubric to evaluate different optical designs in terms of maximizing the use of photons emitted from a CL or ECL assay to improve the LOD. We demonstrate that the majority of cellphone designs reported in the literature are not optimized, in part due to misunderstandings of the optical tradeoffs in collection systems, and in part due to limitations imposed on the designs arising from the use of a mobile phone with a very small lens aperture. Based on the theoretical rubric, we design a new portable reader built using off-the-shelf condenser optics, and demonstrate a nearly 10× performance enhancement compared to prior reports on an ECL assays running on a portable chip.

A sample-to-answer, portable platform for rapid detection of pathogens with a smartphone interface

Emerging and re-emerging infectious diseases pose global threats to human health. Although several conventional diagnostic methods have been widely adopted in the clinic, the long turn-around times of "gold standard" culture-based techniques, as well as the limited sensitivity of lateral-flow strip assays, thwart medical progress. In this study, a smartphone-controlled, automated, and portable system was developed for rapid molecular diagnosis of pathogens (including viruses and bacteria) via the use of a colorimetric loop-mediated isothermal amplification (LAMP) approach on a passive, self-driven microfluidic device. The system was capable of 1) purifying viral or bacterial samples with specific affinity reagents that had been pre-conjugated to magnetic beads, 2) lysing pathogens at low temperatures, 3) executing isothermal nucleic acid amplification, and 4) quantifying the results of colorimetric assays for detection of pathogens with an integrated color sensor. The entire, 40 min analytical process was automatically performed with a novel punching-press mechanism that could be controlled and monitored by a smartphone. As a proof of concept, the influenza A (H1N1) virus and methicillin-resistant Staphylococcus aureus bacteria were used to characterize and optimize the device, and the limits of detection were experimentally found to be 3.2 × 10^-3 hemagglutinating units (HAU) per reaction and 30 colony-forming units (CFU) per reaction, respectively; both such values represent high enough sensitivity for clinical adoption. Moreover, the colorimetric assay could be both qualitative and quantitative for detection of pathogens. This is the first instance of an easy-to-use, automated, and portable system for accurate and sensitive molecular diagnosis of either viruses or bacteria, and it is envisioned that this smartphone-controlled apparatus may serve as a platform for clinical, point-of-care pathogen detection, particularly in resource-limited settings.

The Efficiency of Color Space Channels to Quantify Color and Color Intensity Change in Liquids, pH Strips, and Lateral Flow Assays with Smartphones

Bottom-up, end-user based feed, and food analysis through smartphone quantification of lateral flow assays (LFA) has the potential to cause a paradigm shift in testing capabilities. However, most developed devices do not test the presence of and implications of inter-phone variation. Much discussion remains regarding optimum color space for smartphone colorimetric analyses and, an in-depth comparison of color space performance is missing. Moreover, a light-shielding box is often used to avoid variations caused by background illumination while the use of such a bulky add-on may be avoidable through image background correction. Here, quantification performance of individual channels of RGB, HSV, and LAB color space and ΔRGB was determined for color and color intensity variation using pH strips, filter paper with dropped nanoparticles, and colored solutions. LAB and HSV color space channels never outperformed the best RGB channels in any test. Background correction avoided measurement variation if no direct sunlight was used and functioned more efficiently outside a light-shielding box (prediction errors < 5%/35% for color/color intensity change). The system was validated using various phones for quantification of major allergens (i.e., gluten in buffer, bovine milk in goat milk and goat cheese), and, pH in soil extracts with commercial pH strips and LFA. Inter-phone variation was significant for LFA quantification but low using pH strips (prediction errors < 10% for all six phones compared). Thus, assays based on color change hold the strongest promise for end-user adapted smartphone diagnostics.

Smartphone-Based Device in Exotic Pet Medicine

This article reviews the use of the smartphone in exotic pet medicine. The mobile app is the most instinctive use of the smartphone; however, there are very limited software dedicated to the exotic pet specifically. With an adapter, the smartphone can be attached to a regular endoscope and acts as a small endoscopic unit. Additional devices, such as infrared thermography or ultrasound, can be connected to the smartphone through the micro-USB port. The medical use of the smartphone is still in its infancy in veterinary medicine but can bring several solutions to the exotic pet practitioner and improve point-of-care evaluation.

Advances in Exotic Animal Clinical Pathology

Over the last 10 years, exotic animal clinical pathology has been evolving, improving health assessment in avian, mammal, fish, reptile, and amphibian patients. These advances are reviewed in this article. Species-specific reference intervals for blood parameters are becoming more available (eg, for ionized calcium, endocrine panels, and vitamin D plasmatic concentrations). In addition, new technologies are being developed to facilitate targeted metabolite detection and result acquisition by veterinarians. Novel techniques, biomarkers, and clinical changes related to disease have been described in avian, mammal, fish, reptile and amphibian species. The use of mobile applications may also be helpful.

Clinical training and validation of the LeukoScope: a low-cost, point-of-care device to perform white blood cell and neutrophil counts

A white blood cell (WBC) count with partial differential is an important clinical laboratory test. However, current methods to perform a WBC count and differential are difficult to use at the point of care or too expensive for use in low-resource settings. To meet this need, we developed the LeukoScope: a low-cost system to measure a WBC and neutrophil count from a single drop of blood at the point of care. The LeukoScope is battery powered and has a sample-to-answer time of <5 minutes. A drop of blood from a finger stick is added to a LeukoScope sample cartridge where pre-dried acridine orange fluorescently stains WBCs. The cartridge is then inserted into the LeukoScope reader where a portable fluorescence microscope captures a color image of the sample, which is analyzed to report results to the user. The LeukoScope system was tested at the point of care using fingerprick samples collected from 105 general oncology patients in Houston, TX. Performance of the LeukoScope was compared to that of a HemoCue WBC DIFF performed using the same fingerprick sample; clinical laboratory analysis of a venous blood draw was used as the gold standard in all cases. Bland-Altman analysis showed that the LeukoScope and HemoCue WBC DIFF had similar accuracy for measurement of WBC and neutrophil counts as compared to the gold standard. Seven out of eight patients with abnormal WBC count values were correctly identified using the LeukoScope, while six out of eight were correctly identified using the HemoCue WBC DIFF. Five out of six patients with abnormal neutrophil counts were correctly identified using the LeukoScope, while six of six were correctly identified using the HemoCue WBC DIFF.

Sunlight based handheld smartphone spectrometer

We present a sunlight based handheld smartphone spectrometer. The device first gathers the sunlight to pass through the sample, and then the transmitted light illuminates on a grating to generate spectrum finally recorded by the smartphone monochrome camera. All the optical elements are assembled with the smartphone to integrate a handheld device with the size of 140.2 mm × 67.4 mm × 80.5 mm. Besides, a smartphone application is also developed for automatic spectral calibration, detection, analysis and display. Compared to the white light emitting diode and the halogen lamp, the sunlight has more uniform distribution covering the entire visible spectral range; and the proposed device also avoids the bulky sizes of those broadband light sources. Additionally, the monochrome camera is used instead of the color camera not only to pursue a high spectral resolution as 0.276 nm/pixel but also to avoid the color overlapping. We demonstrate the device capability on detecting avian influenza virus H7N9 and porcine circovirus type 2 antibodies, proving the device has rather high sensitivity similar to the commercial microplate reader. Considering its advantages as compact size, high spectral resolution and detecting sensitivity, it is believed the proposed sunlight based handheld smartphone spectrometer is potential to be broadly applied in on-site detections.

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.

Standardized spectral and radiometric calibration of consumer cameras

Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database.

Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware

The revolution in low-cost consumer photography and computation provides fertile opportunity for a disruptive reduction in the cost of biomedical imaging. Conventional approaches to low-cost microscopy are fundamentally restricted, however, to modest field of view (FOV) and/or resolution. We report a low-cost microscopy technique, implemented with a Raspberry Pi single-board computer and color camera combined with Fourier ptychography (FP), to computationally construct 25-megapixel images with sub-micron resolution. New image-construction techniques were developed to enable the use of the low-cost Bayer color sensor, to compensate for the highly aberrated re-used camera lens and to compensate for misalignments associated with the 3D-printed microscope structure. This high ratio of performance to cost is of particular interest to high-throughput microscopy applications, ranging from drug discovery and digital pathology to health screening in low-income countries. 3D models and assembly instructions of our microscope are made available for open source use.

Cell phone based colorimetric analysis for point-of-care settings

Cell phones show considerable promise for point-of-care (POC) diagnostic procedures because they are accessible, connected, and computationally powerful. Cell phone image processing methods are being developed for the detection and quantification of a wide range of targets, employing methods from microscopy to fluorescence techniques. However, most of the lab-based biological and biochemical assays still lack a robust and repeatable cell phone analogue. Existing solutions require external smartphone hardware to obtain quantitative results, imposing a design tradeoff between accessibility and accuracy. Here, we develop a cell phone imaging algorithm that enables analysis of assays that would typically be evaluated via spectroscopy. The developed technique uses the saturation parameter of hue-saturation-value color space to enable POC diagnosis. Through the analysis of over 10 000 images, we show that the saturation method consistently outperforms existing algorithms under a wide range of operating field conditions. The performance improvement is also proven analytically via the mathematic relationship between the saturation method and existing techniques. The method presented here is a step forward towards the development of POC diagnostics by reducing the required equipment, improving the limit of detection (LOD), and increasing the precision of quantitative results.

Exploiting heat shock protein expression to develop a non-invasive diagnostic tool for breast cancer

Leveraging the unique surface expression of heat shock protein 90 (Hsp90) in breast cancer provides an exciting opportunity to develop rapid diagnostic tests at the point-of-care setting. Hsp90 has previously been shown to have elevated expression levels across all breast cancer receptor subtypes. We have developed a non-destructive strategy using HS-27, a fluorescently-tethered Hsp90 inhibitor, to assay surface Hsp90 expression on intact tissue specimens and validated our approach in clinical samples from breast cancer patients across estrogen receptor positive, Her2-overexpressing, and triple negative receptor subtypes. Utilizing a pre-clinical biopsy model, we optimized three imaging parameters that may affect the specificity of HS-27 based diagnostics – time between tissue excision and staining, agent incubation time, and agent dose, and translated our strategy to clinical breast cancer samples. Findings indicated that HS-27 florescence was highest in tumor tissue, followed by benign tissue, and finally followed by mammoplasty negative control samples. Interestingly, fluorescence in tumor samples was highest in Her2+ and triple negative subtypes, and inversely correlated with the presence of tumor infiltrating lymphocytes indicating that HS-27 fluorescence increases in aggressive breast cancer phenotypes. Development of a Gaussian support vector machine classifier based on HS-27 fluorescence features resulted in a sensitivity and specificity of 82% and 100% respectively when classifying tumor and benign conditions, setting the stage for rapid and automated tissue diagnosis at the point-of-care.

cellSTORM-Cost-effective super-resolution on a cellphone using dSTORM

High optical resolution in microscopy usually goes along with costly hardware components, such as lenses, mechanical setups and cameras. Several studies proved that Single Molecular Localization Microscopy can be made affordable, relying on off-the-shelf optical components and industry grade CMOS cameras. Recent technological advantages have yielded consumer-grade camera devices with surprisingly good performance. The camera sensors of smartphones have benefited of this development. Combined with computing power smartphones provide a fantastic opportunity for “imaging on a budget”. Here we show that a consumer cellphone is capable of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we used a trained image-to-image generative adversarial network (GAN) to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance directly on the smartphone. We believe that “cellSTORM” paves the way to make super-resolution microscopy not only affordable but available due to the ubiquity of cellphone cameras.

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.

Open Source 3D-printed focussing mechanism for cellphone-based cellular microscopy

The need to improve access to microscopes in low-resource and educational settings coupled with the global proliferation of camera-enabled cellphones has recently led to an explosion in new developments in portable, low-cost microscopy. The availability of accurate ball lenses has resulted in many variants of van Leeuwenhoek-like microscopes. Combined with cellphones, they have the potential for use as portable microscopes in education and clinics. The need for reproducibility in such applications implies that control over focus is critical. Here, we describe a 3D-printed focussing mechanism based on a rack and pinion mechanism, coupled to a ball lens- based microscope. We quantify the time-stability of the focussing mechanism through an edge-based contrast measure used in autofocus cameras and apply it to 'thin smear' blood sample infected with Plasmodium as well as onion skin cells. We show that stability of the z-focus is in the micrometre range. This development could, we believe, serve to further enhance the utility of a low-cost and robust microscope and encourage further developments in field microscopes based on the Open Source principle. LAY DESCRIPTION: The wide spread of cellphones with cameras makes them an attractive platform for digital microscopy. Such microscopes could help improve microscope access in clinics and classrooms in the form of 'field microscopes', if they could be adapted for imaging cells. We integrate a 3D printed focussing mechanism made with recyclable plastic with ball-lens microscope of the Leeuwenhoek type. We demonstrate how the device can help stabilise to a focal plane for acquiring movies of a thin-smear of blood infected with Plasmodium and onion skin cells using a cellphone. The stability of focus is expectedly less precise as compared to research-grade microscopes, but is of the range of a few micrometers. We believe, the focussing device demonstrates it is possible to obtain reliable and reproducible images of typical samples used in clinics and classrooms. By making the design files of this device open-source we believe it could serve as a small step in improved, affordable and accurate 'field microscopes'.

Ultra-sensitive microfluidic wearable strain sensor for intraocular pressure monitoring

Wearable technologies have potential to transform healthcare by providing continuous measurements of physiological parameters. Sensors that passively monitor physiological pressure without using electronic components are ideal for wearable contact lenses because they are easy to interface with the cornea and the external environment. Here, we report a passive integrated microfluidic sensor with a novel transduction mechanism that converts small strain changes into a large fluidic volume expansion, detectable by a smart-phone camera. The optimization of the sensor architecture and material properties results in a linear and stable sensor response. We have shown that the sensor has a detection limit of <0.06% for uniaxial and <0.004% for biaxial strain. We embedded our sensor in silicone contact lenses and measured the intraocular pressure induced strain in porcine eyes in the physiological range. The sensor's continuous operation capability for >19 hours and a lifetime reaching >7 months demonstrate its potential for long-term ophthalmic monitoring applications.

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.

Diagnostic utility of flexible fiberoptic nasopharyngolaryngoscopy recorded onto a smartphone: A pilot study

Objectives

To evaluate the diagnostic accuracy of flexible fiberoptic examinations of the larynx recorded onto smartphones.

Methods

Prospective, blinded study of inpatients requiring laryngoscopy. A live exam was performed, then a smartphone was attached to the endoscope using a novel coupling device and the same examination was recorded. The live and recorded exams were evaluated by two laryngologists, each blinded to the findings of the other.

Results

Eighteen subjects were evaluated. Evaluation of airway patency was identical (Kappa = 1.0 [1, 1]). Evaluation of vocal cord motion was identical for 14 subjects: 9 normal, 3 paretic, 2 paralytic (Kappa = 0.69 [0.38, 1]).

Conclusion

There is high correlation between laryngeal diagnoses using live flexible fiberoptic laryngoscopy and recordings using a coupling device to transfer the recordings on to smartphones. Critical findings such as airway patency and vocal fold motion showed the highest correlation.

Development of Whole Slide Imaging on Smartphones and Evaluation With ThinPrep Cytology Test Samples: Follow-Up Study

Background

The smartphone-based whole slide imaging (WSI) system represents a low-cost and effective alternative to automatic scanners for telepathology. In a previous study, the development of one such solution, named scalable whole slide imaging (sWSI), was presented and analyzed. A clinical evaluation of its iOS version with 100 frozen section samples verified the diagnosis-readiness of the produced virtual slides.

Objective

The first aim of this study was to delve into the quantifying issues encountered in the development of an Android version. It should also provide insights into future high-resolution real-time feedback medical imaging apps on Android and invoke the awareness of smartphone manufacturers for collaboration. The second aim of this study was to further verify the clinical value of sWSI with cytology samples. This type is different from the frozen section samples in that they require finer detail on the cellular level.

Methods

During sWSI development on Android, it was discovered that many models do not support uncompressed camera pixel data with sufficient resolution and full field of view. The proportion of models supporting the optimal format was estimated in a test on 200 mainstream Android models. Other factors, including slower processing speed and camera preview freezing, also led to inferior performance of sWSI on Android compared with the iOS version. The processing speed was mostly determined by the central processing unit frequency in theory, and the relationship was investigated in the 200-model simulation experiment with physical devices. The camera preview freezing was caused by the lag between triggering photo capture and resuming preview. In the clinical evaluation, 100 ThinPrep cytology test samples covering 6 diseases were scanned with sWSI and compared against the ground truth of optical microscopy.

Results

Among the tested Android models, only 3.0% (6/200) provided an optimal data format, meeting all criteria of quality and efficiency. The image-processing speed demonstrated a positive relationship with the central processing unit frequency but to a smaller degree than expected and was highly model-dependent. The virtual slides produced by sWSI on Android and iOS of ThinPrep cytology test samples achieved similar high quality. Using optical microscopy as the ground truth, pathologists made a correct diagnosis on 87.5% (175/200) of the cases with sWSI virtual slides. Depending on the sWSI version and the pathologist in charge, the kappa value varied between .70 and .82. All participating pathologists considered the quality of the sWSI virtual slides in the experiment to be adequate for routine usage.

Conclusions

Limited by hardware and operating system support, the performance of sWSI on mainstream Android smartphones did not fully match the iOS version. However, in practice, this difference was not significant, and both were adequate for digitizing most of the sample types for telepathology consultation.

Image analysis and machine learning for detecting malaria

Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts at fighting the disease. One of the barriers toward a successful mortality reduction has been inadequate malaria diagnosis in particular. To improve diagnosis, image analysis software and machine learning methods have been used to quantify parasitemia in microscopic blood slides. This article gives an overview of these techniques and discusses the current developments in image analysis and machine learning for microscopic malaria diagnosis. We organize the different approaches published in the literature according to the techniques used for imaging, image preprocessing, parasite detection and cell segmentation, feature computation, and automatic cell classification. Readers will find the different techniques listed in tables, with the relevant articles cited next to them, for both thin and thick blood smear images. We also discussed the latest developments in sections devoted to deep learning and smartphone technology for future malaria diagnosis.