Smartphone-based Imaging Research Articles

Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning

Counterfeiting is a pervasive issue in the collectible card market, posing significant risks to collectors, investors, and the industry at large. The trade of counterfeit cards not only undermines the value of genuine collectibles but also deceives consumers who invest their resources into fraudulent items. Despite advancements in counterfeit detection technologies, many counterfeit cards remain undiagnosed and continue to circulate in the marketplace, primarily due to the reliance on traditional methods of verification, which are often time-consuming and require expert evaluation. This study aims to leverage the increasing capabilities of widely available consumer technologies, specifically smartphones, to develop an innovative approach for the early detection of counterfeit collectible cards. By utilizing the multi-image signals acquired from smartphone cameras, we hypothesize that significant differences in color, type font, and material reflectiveness associated with counterfeit cards can be identified through machine learning techniques, particularly convolutional neural networks (CNNs). To evaluate this hypothesis, we analyzed a comprehensive dataset of 22,298 individual trading cards collected through the Digital Grading Company smartphone app. The dataset consisted of user-submitted images, which were systematically categorized into training, development, and test datasets to facilitate model training and validation. A robust 34-layer CNN architecture was employed to analyze these multi-image signals and predict the prevalence of counterfeit cards. The model's performance was measured using the area under the receiver operating characteristic curve (AUC), providing a quantitative assessment of its discriminatory ability. Our results revealed that of the total cards analyzed, 6.0% were identified as counterfeit, with the CNN model achieving an AUC of 0.772 (95% CI 0.747 - 0.797) in the test dataset. This indicates a reasonable level of discrimination in detecting counterfeit cards based solely on the multi-image data. The findings suggest that deep learning technologies can significantly enhance counterfeit detection processes, providing collectors and industry stakeholders with a powerful tool to combat fraud. This study represents the first proof-of-concept demonstration of utilizing smartphone-based imaging for the detection of counterfeits in collectible trading cards. By validating the effectiveness of deep learning in this context, we pave the way for future research that can explore more sophisticated algorithms and techniques to further improve detection accuracy. Ultimately, our approach could lead to the development of accessible applications for collectors and consumers, empowering them to make informed decisions and protect their investments in the collectible market.

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

A two-way analytical investigation of ancient gold coins: Elemental and colorimetric description of precious materials

The investigation of ancient artifacts is often constrained by their scarce availability and high protection and custody protocols. Among these, coinage represents an especially valuable kind-of-samples given their uniqueness and the subjacent information that is hidden behind their composition. Their analysis are often carried out using non-destructive techniques in order to avoid any alteration of the samples. In the field of Cultural Heritage analysis, smartphone-based methodologies have experienced a significant increase during the last few years, given their wide availability and ability to yield fast results. However, their analytical application demands a thorough and careful tuning during the methodology optimization. In this work, 21 historical gold and golden coins spanning a historical period of more than 2000 years have been analytically investigated. To that end, a two-fold approach has been implemented: first, the elemental composition has been analysed using portable X-ray fluorescence; and second, an innovative smartphone-based imaging method has been applied to measure their colour. Results allowed to describe the coins from their elemental profile, identifying some potentially debased ones, as well as some others not containing any gold. When possible, the results have been compared to previously reported cases, but our samples include some previously unreported cases representing new insights. All in all, this article provides new analytical data on unanalysed unique historical samples, in terms of their elemental profile and colorimetric properties, making use of an innovative, non-invasive nor destructive, fast and affordable colorimetric smartphone-based method to characterise historical coins.

A sensitive, portable, and smartphone-based whole-cell biosensor device for salicylic acid monitoring

Considerable effort has been invested in developing salicylic acid (SA) biosensors for various application purposes. Here, by engineering the sensing modules and host cell chassis, we have gradually optimized the NahR-Psal/Pr-based SA biosensor, increasing the sensitivity and maximum output by 17.2-fold and 9.4-fold, respectively, and improving the detection limit by 800-fold, from 80 μM to 0.1 μM. A portable SA sensing device was constructed by embedding a gelatin-based hydrogel containing an optimized biosensor into the perforations of tape adhered to glass slide, which allowed good determination of SA in the range of 0.1 μM–10 μM. Then, we developed a customized smartphone App to measure the fluorescence intensity of each perforation and automatically calculate the corresponding SA concentration so that we could detect SA concentrations in real cosmetic samples. We anticipate that this smartphone-based imaging biosensor, with its compact size, higher sensitivity, cost-effectiveness, and easy data transfer, will be useful for long-term monitoring of SA.

Smartphone-based imaging colorimetric assay for monitoring the quality of curcumin in turmeric powder.

This research developed a colorimetric assay for semi-quantitative curcumin detection. The screening test was performed using a ferric chloride to form a brownish color which was further used to evaluate the amount of curcumin in the turmeric powder samples. The quantitative assay was performed based on the color intensity of the curcumin target using a smartphone digital image colorimetry with a developed lightbox constructed with a white light-emitting diodes (LED) light source as the measurement device. Images in red, green, and blue (RGB) color were processed to obtain relevant colors from the image and the color values were used to analyze curcumin concentrations. The intensity of the ΔB was correlated to the concentration of curcumin with high sensitivity. The method showed a linear range between 0.25 and 5mg L-1 with the LOD and LOQ of 0.12 and 0.41mg L-1, respectively. Sample analysis was carried out in turmeric powders. Curcumin in turmeric powder samples was simply extracted using acetonitrile followed by dilution 100 times for sample preparation. The accuracy was tested by spiking 0.25, 1.00, and 4.00mg L-1 of standard curcumin into the turmeric sample solution. The average percentage recoveries were acceptable in all samples (90-104%). The method was validated by comparing the results obtained from the proposed method and high-performance liquid chromatography (HPLC). There was no statistically significant difference between the two methods (P = 0.05).

Microfluidic Immunosensing Platform Based on a Rolling Circle Amplification-Assisted DNA Dendrimer Probe for Portable and Sensitive Detection of Allergen-Specific IgE.

The robust point-of-care platform for sensitive, multiplexed, and affordable detection of allergen-specific IgE (sIgE) is an urgent demand in component-resolved diagnostics. Here, we developed a microfluidic immunosensing platform based on a rolling circle amplification-assisted DNA dendrimer probe for sensitive detection of multiple sIgEs. The versatile multichannel microfluidic whole blood analytical device integrates cell filtration, recombinant antigen-modified magnetic enrichment, and DNA dendrimer probe-amplified signal transduction for portable on-chip analysis. Three sIgEs against common oyster allergens were simultaneously detected in blood samples by simple smartphone-based imaging without any pretreatment. The quantitative detection of multiple allergen-specific antibodies on the platform was achieved with limits of detection of less than 50 pg/mL, exhibiting superior sensitivity compared to most point-of-care testing. The detection results of 55 serum samples and 4 whole blood samples were 100% consistent with the ELISA results, confirming the accuracy and stability of our platform. Additionally, the reversible combination of hexahistidine6-tag and Ni-IMAC magbead was elegantly utilized on the immunosensing platform for desired reversibility. With the advantages of general applicability, high sensitivity, and reversibility, the DNA dendrimer-based microfluidic immunosensing platform provides great potential for the portable detection of immune proteins as a point-of-care platform in disease diagnostics and biological analysis.

Ensemble fusion model for improved lung abnormality classification: Leveraging pre-trained models

Lung abnormalities pose significant health concerns, underscoring the need for swift and accurate diagnoses to facilitate timely medical intervention. This study introduces a novel methodology for the sub-classification of lung abnormalities within chest X-rays captured via smartphones. An accurate and timely diagnosis of lung abnormalities is essential for the successful implementation of appropriate therapy. In this paper, we propose a novel approach using a Convolutional neural network (CNN) with three maximum pooling layers and early fusion for sub-classifying lung abnormalities from chest Xrays. Based on the kind of abnormality, the CheXpert dataset is divided into 13 sub-classes, each of which is trained using a different sub-model. An early fusion procedure is then used to integrate the outputs of the sub-model.•3M-CNN (Method 1): We employed a Convolutional Neural Network (CNN) with three max pooling layers and an early fusion strategy to train dedicated sub-models for each of the 13 distinct sub-classes of lung abnormalities using the CheXpert dataset.•Ensemble Model (Method 2): Our ‘Ensemble model’ integrated the outputs of the trained sub-models, providing a powerful approach for the sub-classification of lung abnormalities.•Exceptional Accuracy: Our ‘3M-CNN’ and ‘fused model’ achieved an accuracy of 98.79%, surpassing established methodologies, which is beneficial in resource-constrained environments embracing smartphone-based imaging.

Smartphone-Based Fluorescent Profiling of Quaternary MicroRNAs in Urine for Rapid Diagnosis of Urological Cancers Using a Multiplexed Isothermal Exponential Amplification Reaction.

Urological cancers such as bladder or prostate cancer represent one of the most malignant tumors that accounts for an extremely high mortality. However, conventionally standard diagnostics for urological cancers are hardly available in low-resource settings. We developed herein a hand-held fluorescent imaging platform by integrating a multiplexed isothermal exponential amplification reaction (EXPAR) with a microgel-enriched methodology for sensitive profiling of quaternary microRNAs (miRNAs) in urine and quick diagnosis of urological cancers at the early stage. The target miRNA mixtures in the urine underwent four parallel EXPARs without cross-reactivity, followed by surface concentration and hybridization by the encoded polyacrylamide microgels. This mix-and-read strategy allowed for one-pot analysis of several key miRNAs simultaneously and provided 5-fold enhancement in fluorescent detection sensitivities compared to the individual EXPAR-based assays. Four urinary miRNAs (let-7a, miRNA-155, -223, and -143) could be quantitatively determined in a wide linear range from 50 fM to 30 nM, with the limits of detection at femtomolar levels. Using a smartphone-based imaging microreader, healthy and cancerous cohorts with prostate, bladder, and renal cell cancers could be discriminated in 30 min with the accuracy >83% using linear discriminant analysis. The developed detection platform has proven to be a portable, noninvasive, and useful complement to the toolbox for miRNA-based liquid biopsies, which holds immense potential and advantage for regular and large-scale applications in early cancer diagnosis.

A simple yet effective colorimetric assay for nitrite based on nitration of a near-infrared (NIR) absorbing dye IR780

Nitrite (NO2−) is a unique active ion involved in the nitrogen biochemical cycle which is very important to life on earth. Besides, it is indispensable additive to preserve nutrients in diverse food products. However, the concentrations above allowable limits pose considerable threat to human health and require urgent attention. In this study, we report a novel detection strategy by using IR780 dye as a near-infrared (NIR) probe which shows unexpected colorimetric response to NO2− ions with excellent selectivity, a rapid response (<5 s) and a low detection limit (227 nM). The probe produces the distinct color change from chartreuse to violet with a 220 nm of large hypsochromic (blue) shift caused by the nitration of IR780 in the presence of NO2− ions. Smartphone-based imaging has been further employed for real time and on-site visual detection of NO2− ions which will widen the application area of our system in environmental safety and food quality. In addition, the probe IR780 has shown excellent sensing performance in complex environments such as tap water and processed meat products with high recovery rates (85–113 %) and reproducibility (RSD < 5 %). Overall, the results demonstrate that the new system is capable of sensing NO2− ions in water resources as well as complex food matrices with high sensitivity and selectivity.

Emission Onset Time-Adjustable Chemiluminescent Gold Nanoparticles with Ultrastrong Emission for Smartphone-Based Immunoassay of Severe Acute Respiratory Syndrome Coronavirus 2 Antigen.

Recently, our group reported a chemical timer approach to manipulate the onset time of chemiluminescence (CL) emission. However, it is still in the proof-of-concept stage, and its analytical applications have not been explored yet. Nanomaterials have merits of good catalytic effect, large specific surface area, good biocompatibility, and ease of self-assembly, which are ideal for constructing analytical-interfaces for bioassays. Herein, an emission onset time-adjustable chemiluminescent L012-Au/Mn2+ was synthesized for the first time by modifying Mn2+ on the surface of L012-protected gold nanoparticle. By using H2O2 and NaHCO3 as coreactants, L012-Au/Mn2+ could not only generate an ultrastrong and long-time CL emission but also its CL emission onset time could be adjusted by the addition of thiourea, which could effectively eliminate interference from the addition of coreactants, shorten the exposure time, reduce the detection background, and finally achieve high sensitivity CL imaging analysis. On this basis, a label-free CL immunoassay was constructed with a smartphone-based imaging system for high-throughput and sensitive determination of severe acute respiratory syndrome coronavirus 2 nucleocapsid (N) protein. The CL image of the immunoassay with different concentrations of N proteins was captured in one photograph 100 s after the injection of H2O2 with a short exposure time of 0.5 s. The immunoassay showed good linearity over the concentration range of 1 pg/mL to 10 ng/mL with a detection limit of 0.13 pg/mL, which was much lower than the reported CCD imaging detection method. In addition, it showed good selectivity and stability and was successfully applied in serum samples from healthy individuals and COVID-19 rehabilitation patients.

A Smartphone-Based Imaging Method for C. elegans Lawn Avoidance Assay.

When exposed to toxic or pathogenic bacteria, the nematode Caenorhabditis elegans displays a learned lawn avoidance behavior, in which the worms gradually leave their food source and prefer to remain outside the bacterial lawn. The assay is an easy way to test the worms' ability to sense external or internal cues to properly respond to harmful conditions. Though a simple assay, counting is time consuming, particularly with multiple samples, and assay durations that span overnight are inconvenient for researchers. An imaging system that can image many plates over a long period is useful but costly. Here, we describe a smartphone-based imaging method to record lawn avoidance in C. elegans. The method requires only a smartphone and a light emitting diode (LED) light box, to serve as a transmitted light source. Using free time-lapse camera applications, each phone can image up to six plates, with sufficient sharpness and contrast to manually count worms outside the lawn. The resulting movies are processed into 10 s audio video interleave (AVI) files for every hourly time point, then cropped to show each single plate to make them more amenable for counting. This method is a cost-effective way for those looking to examine avoidance defects and can potentially be extended to other C. elegans assays.

A portable smartphone-based imaging surface plasmon resonance biosensor for allergen detection in plant-based milks

Food allergies are hypersensitivity immune responses triggered by (traces of) allergenic compounds in foods and drinks. The recent trend towards plant-based and lactose-free diets has driven an increased consumption of plant-based milks (PBMs) with the risk of cross-contamination of various allergenic plant-based proteins during the food manufacturing process. Conventional allergen screening is usually performed in the laboratory, but portable biosensors for on-site screening of food allergens at the production site could improve quality control and food safety. Here, we developed a portable smartphone imaging surface plasmon resonance (iSPR) biosensor composed of a 3D-printed microfluidic SPR chip for the detection of total hazelnut protein (THP) in commercial PBMs and compared its instrumentation and analytical performance with a conventional benchtop SPR. The smartphone iSPR shows similar characteristic sensorgrams compared with the benchtop SPR and enables the detection of trace levels of THP in spiked PBMs with the lowest tested concentration of 0.625 μg/mL THP. The smartphone iSPR achieved LoDs of 0.53, 0.16, 0.14, 0.06, and 0.04 μg/mL THP in 10x-diluted soy, oat, rice, coconut, and almond PBMs, respectively, with good correlation with the conventional benchtop SPR system (R2 0.950–0.991). The portability and miniaturized characteristics of the smartphone iSPR biosensor platform make it promising for the future on-site detection of food allergens by food producers.

A novel smartphone scleral-image based tool for assessing jaundice in decompensated cirrhosis patients.

Serum bilirubin is an established marker of liver disease. Reliable tools for non-invasive assessment of jaundice in cirrhosis patients, at risk of clinical decompensation, are highly desirable. While smartphone-based imaging has been described in neonatal jaundice, it has not been investigated in advanced cirrhosis patients. We included 46 hospitalized patients with acute cirrhosis decompensation and jaundice. Scleral images using an Android smartphone were taken to derive "Scleral Color Values (SCV)," which were matched with same day serum bilirubin measurements. In 29 patients, repeat SCV and bilirubin measurements were performed over time. We analyzed the relationship of SCV and its dynamics with serum bilirubin, clinical scores, and patient outcomes. Of 46 patients, 26 (57%) had alcoholic hepatitis as the decompensation precipitant. Seven patients died during admission; a further 12 following hospital discharge. SCV had an excellent linear correlation with serum bilirubin (rho=0.90, P<0.001); changes in SCV and serum bilirubin across different time points, were also closely associated (rho=0.77, P<0.001). SCV correlated significantly with CLIF Consortium Acute Decompensation score (rho=0.38, P<0.001) and grade of Acute-on-Chronic Liver Failure (rho=0.42, P=0.039). SCV was higher in patients who died, however, not significantly (86.1 [IQR 83.0-89.7] vs 82.3 [IQR 78.5-83.3], P=0.22). The associations of SCV with clinical parameters mirrored those of serum bilirubin. Smartphone-based assessment of jaundice shows excellent concordance with serum bilirubin and is associated with clinical parameters in acute cirrhosis decompensation. This approach offers promise for remote assessment of cirrhosis patients at-risk of decompensation, post hospital discharge.

Evolving Visions: A Comprehensive Review of Contemporary and Emerging Technologies in Anterior Segment Imaging

Background: The anterior segment of the eye, which includes the cornea, iris, ciliary body, and lens, has always been critical for vision. Over the years, imaging of this segment has significantly evolved, with the development of various modalities to aid in the diagnosis and management of ocular conditions. The aim of this review is to provide a comprehensive overview of these contemporary imaging modalities. Methods: A thorough literature review was conducted, focusing on the technical aspects and clinical applications of various imaging modalities, including slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), ultrasound biomicroscopy (UBM), Scheimpflug imaging, corneal topography and tomography, in vivo confocal microscopy (IVCM), specular microscopy, and anterior segment photography and videography. Results: Each imaging modality offers unique advantages and has specific applications in diagnosing and managing various ocular conditions. Emerging technologies, such as intense pulse light (IPL), contoured prism spectacle lenses, smartphone-based imaging, artificial intelligence (AI), and adaptive optics show promise in revolutionizing anterior segment imaging. Conclusion: The advancements in anterior segment imaging have significantly improved our ability to diagnose and manage ocular conditions. These technologies offer practical implications in improving patient outcomes, enhancing clinical efficiency, and opening new avenues for research. As we continue to refine these technologies and develop new ones, we can expect to see further improvements in patient care.

Agreement of a Novel Artificial Intelligence Software With Optical Coherence Tomography and Manual Grading of the Optic Disc in Glaucoma.

The offline artificial intelligence (AI) on a smartphone-based fundus camera shows good agreement and correlation with the vertical cup-to-disc ratio (vCDR) from the spectral-domain optical coherence tomography (SD-OCT) and manual grading by experts. The purpose of this study is to assess the agreement of vCDR measured by a new AI software from optic disc images obtained using a validated smartphone-based imaging device, with SD-OCT vCDR measurements, and manual grading by experts on a stereoscopic fundus camera. In a prospective, cross-sectional study, participants above 18 years (Glaucoma and normal) underwent a dilated fundus evaluation, followed by optic disc imaging including a 42-degree monoscopic disc-centered image (Remidio NM-FOP-10), a 30-degree stereoscopic disc-centered image (Kowa nonmyd WX-3D desktop fundus camera), and disc analysis (Cirrus SD-OCT). Remidio FOP images were analyzed for vCDR using the new AI software, and Kowa stereoscopic images were manually graded by 3 fellowship-trained glaucoma specialists. We included 473 eyes of 244 participants. The vCDR values from the new AI software showed strong agreement with SD-OCT measurements [95% limits of agreement (LoA)=-0.13 to 0.16]. The agreement with SD-OCT was marginally better in eyes with higher vCDR (95% LoA=-0.15 to 0.12 for vCDR>0.8). Interclass correlation coefficient was 0.90 (95% CI, 0.88-0.91). The vCDR values from AI software showed a good correlation with the manual segmentation by experts (interclass correlation coefficient=0.89, 95% CI, 0.87-0.91) on stereoscopic images (95% LoA=-0.18 to 0.11) with agreement better for eyes with vCDR>0.8 (LoA=-0.12 to 0.08). The new AI software vCDR measurements had an excellent agreement and correlation with the SD-OCT and manual grading. The ability of the Medios AI to work offline, without requiring cloud-based inferencing, is an added advantage.

Well-Designed Smartphone-Based Imaging Biosensor

With the development of hardware and software for smartphones, more and more well-designed smartphone-based imaging biosensors have been created and broadly applied in point-of-care testing (POCT). Imaging biosensors can get clear images through the high pixel density of smartphones’ camera systems. And smartphones also provide a chance for imaging processing thanks to smartphones' central processing units (CPUs) and graphics processing units (GPUs). Different approaches have extensively explored smartphone-based imaging biosensors. The commonly used imaging methods are generally implemented by the bright field with the light source or by fluorescence with a fluorescence microscope. Smartphones have enabled the widespread application of imaging-based methods in clinical chemistry, environmental monitoring, flow cytometry, food analysis, drug screening, and medical diagnostics. In detail, this article discusses various imaging biosensors and specific applications of smartphone-based imaging biosensors for bright-field imaging and fluorescence bioimaging. Meanwhile, the opportunities and challenges of smartphone-based imaging biosensors are also analyzed here.

Digital Droplet Loop-Mediated Isothermal Amplification Featuring a Molecular Beacon Assay, 3D Printed Droplet Generation, and Smartphone Imaging for Sequence-Specific DNA Detection.

Nucleic acid detection is widely used in the amplification and quantitation of nucleic acids from biological samples. While polymerase chain reaction (PCR) enjoys great popularity, expensive thermal cyclers are required for precise temperature control. Loop-mediated isothermal amplification (LAMP) enables highly sensitive, rapid, and low-cost amplification of nucleic acids at constant temperatures. LAMP detection often relies on double-stranded DNA-binding dyes or metal indicators that lack sequence selectivity. Molecular beacons (MBs) are hairpin-shaped oligonucleotide probes whose sequence specificity in LAMP provides the capability of differentiating between single-nucleotide polymorphisms (SNPs). Digital droplet LAMP (ddLAMP) enables a large number of independent LAMP reactions to be performed and provides quantification of target DNA sequences. However, a major challenge with ddLAMP is the requirement of expensive droplet generators to form homogeneous microdroplets. In this study, we demonstrate for the first time that a three-dimensional (3D) printed droplet generation platform can be coupled to a LAMP assay featuring MBs as sequence-specific probes. The low-cost 3D printed droplet generator system was designed, and its customizability was demonstrated in the formation of monodisperse ddLAMP assay-in-oil microdroplets. Additionally, a smartphone-based imaging system is demonstrated to increase accessibility for point-of-care applications. The MB-ddLAMP assay is shown to discriminate between two SNPs at various amplification temperatures to afford a useful platform for sequence-specific, sensitive, and accurate DNA quantification. This work expands the utility of MBs to ddLAMP for quantitative studies in the detection of SNPs and exploits the customizability of 3D printing technologies to optimize the homogeneity, size, and volume of oil-in-water microdroplets.

An open-source smartphone app for the quantitative evaluation of thin-layer chromatographic analyses in medicine quality screening

Substandard and falsified medicines present a serious threat to public health. Simple, low-cost screening tools are important in the identification of such products in low- and middle-income countries. In the present study, a smartphone-based imaging software was developed for the quantification of thin-layer chromatographic (TLC) analyses. A performance evaluation of this tool in the TLC analysis of 14 active pharmaceutical ingredients according to the procedures of the Global Pharma Health Fund (GPHF) Minilab was carried out, following international guidelines and assessing accuracy, repeatability, intermediate precision, specificity, linearity, range and robustness of the method. Relative standard deviations of 2.79% and 4.46% between individual measurements were observed in the assessments of repeatability and intermediate precision, respectively. Small deliberate variations of the conditions hardly affected the results. A locally producible wooden box was designed which ensures TLC photography under standardized conditions and shielding from ambient light. Photography and image analysis were carried out with a low-cost Android-based smartphone. The app allows to share TLC photos and quantification results using messaging apps, e-mail, cable or Bluetooth connections, or to upload them to a cloud. The app is available free of charge as General Public License (GPL) open-source software, and interested individuals or organizations are welcome to use and/or to further improve this software.

Slit-lamp based intraocular lens microscope - A novel technique of rapid office-based microscopy.

The emergence of smartphone-based imaging devices has been a boon in the field of ophthalmology, especially in obtaining high-quality ocular images. They can be specialized and utilized for imaging-specific regions of the eye. Among the multitude of applications of smartphone-based imaging, one of the upcoming major use is to image the microbiological world. Previous few reports have described attaching magnifying lenses of various types to the smartphone camera and transforming it into a microscope for imaging fungal hyphae and ocular surface parasites. We describe a novel technique of attaching the smartphone-based intraocular lens microscope (IOLSCOPE) to the slit lamp, thereby utilizing the slit lamp joystick for moving the smartphone over the concerned slide specimen to make it steady and obtain images of high resolution. This innovative do-it-yourself novel modification is especially useful in peripheral centers, vision centers, and local clinics for immediate screening and identification of microbial pathogens such as fungi and ocular surface parasites.

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.