Smartphone-based Imaging System Research Articles

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.

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.

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.

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

BackgroundApproximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing–based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations.ObjectiveIn this study, we demonstrate a new quantitative CC screening technique and implement a machine learning algorithm for smartphone-based endoscopic VIA. We also evaluated the diagnostic performance and practicability of the approach based on the results compared to the gold standard and from physicians’ interpretation.MethodsA smartphone-based endoscope system was developed and applied to the VIA screening. A total of 20 patients were recruited for this study to evaluate the system. Overall, five were healthy, and 15 were patients who had shown a low to high grade of cervical intraepithelial neoplasia (CIN) from both colposcopy and cytology tests. Endoscopic VIA images were obtained before a loop electrosurgical excision procedure for patients with abnormal tissues, and their histology tissues were collected. Endoscopic VIA images were assessed by four expert physicians relative to the gold standard of histopathology. Also, VIA features were extracted from multiple steps of image processing techniques to find the differences between abnormal (CIN2+) and normal (≤CIN1). By using the extracted features, the performance of different machine learning classifiers, such as k-nearest neighbors (KNN), support vector machine, and decision tree (DT), were compared to find the best algorithm for VIA. After determining the best performing classifying model, it was used to evaluate the screening performance of VIA.ResultsAn average accuracy of 78%, with a Cohen kappa of 0.571, was observed for the evaluation of the system by four physicians. Through image processing, 240 sliced images were obtained from the cervicogram at each clock position, and five features of VIA were extracted. Among the three models, KNN showed the best performance for finding VIA within holdout 10-fold cross-validation, with an accuracy of 78.3%, area under the curve of 0.807, a specificity of 80.3%, and a sensitivity of 75.0%, respectively. The trained model performed using an unprovided data set resulted in an accuracy of 80.8%, specificity of 84.1%, and sensitivity of 71.9%. Predictions were visualized with intuitive color labels, indicating the normal/abnormal tissue using a circular clock-type segmentation. Calculating the overlapped abnormal tissues between the gold standard and predicted value, the KNN model overperformed the average assessments of physicians for finding VIA.ConclusionsWe explored the potential of the smartphone-based endoscopic VIA as an evaluation technique and used the cervicogram to evaluate normal/abnormal tissue using machine learning techniques. The results of this study demonstrate its potential as a screening tool in low-resource settings.

A multicolor multiplex lateral flow assay for high-sensitivity analyte detection using persistent luminescent nanophosphors.

Incorporating two persistent luminescent nanophosphors (PLNPs), green-emitting SrAl2O4:Eu2+,Dy3+ (SAO) and blue-emitting (Sr0.625Ba0.375)2MgSi2O7:Eu2+,Dy3+ (SBMSO), in a single lateral flow assay (LFA) establishes a luminescence-based, multiplex point-of-need test capable of simultaneously detecting two different analytes in a single sample. The advantages of this system are the high sensitivity and photostability of PLNPs, while only requiring access to minimal hardware and a smartphone for signal detection. The PLNPs were obtained by first wet milling bulk synthesized phosphor powders, followed by fractionation using differential centrifugal sedimentation to obtain monodisperse nanoparticles. A modified Stöber process was then employed to encapsulate the nanoparticles in a water-stable silica shell followed by attaching antibodies to the particles' surfaces using reductive amination chemistry. The resulting PLNPs were incorporated in an LFA to concurrently detect two independent model analytes, prostate-specific antigen (PSA) and human chorionic gonadotropin (hCG). The multicolor-multiplex PLNP-based assays were finally imaged using a smartphone-based imaging system with excellent detection limits (0.1 ng mL-1 of PSA and 1 ng mL-1 of hCG) that are competitive with commercially available LFAs.

Image-based food portion size estimation using a smartphone without a fiducial marker.

Current approaches to food volume estimation require the person to carry a fiducial marker (e.g. a checkerboard card), to be placed next to the food before taking a picture. This procedure is inconvenient and post-processing of the food picture is time-consuming and sometimes inaccurate. These problems keep people from using the smartphone for self-administered dietary assessment. The current bioengineering study presents a novel smartphone-based imaging approach to table-side estimation of food volume which overcomes current limitations. We present a new method for food volume estimation without a fiducial marker. Our mathematical model indicates that, using a special picture-taking strategy, the smartphone-based imaging system can be calibrated adequately if the physical length of the smartphone and the output of the motion sensor within the device are known. We also present and test a new virtual reality method for food volume estimation using the International Food Unit™ and a training process for error control. Our pilot study, with sixty-nine participants and fifteen foods, indicates that the fiducial-marker-free approach is valid and that the training improves estimation accuracy significantly (P0·05). Elimination of a fiducial marker and application of virtual reality, the International Food Unit™ and an automated training allowed quick food volume estimation and control of the estimation error. The estimated volume could be used to search a nutrient database and determine energy and nutrients in the diet.

ASSESSMENT OF SMARTPHONE-BASED TECHNOLOGY FOR REMOTE ENVIRONMENTAL MONITORING AND ITS DEVELOPMENT

Many countries are increasing their research on monitoring technology to identify and systematically manage various domestic changes. In particular, the need for remote monitoring is increasing in response to climatic disasters, such as flooding, storms, and rising tides caused by global warming. We developed a smartphone-based environmental monitoring system that enables remote monitoring in any place and at any time. The overall system is composed of a 24-hour smartphone-based imaging system, a monitoring information management system to receive the monitoring information, and stereo image rectification software that provides lens distortion correction, geometric correction, and stereo matching of the monitoring images. The system was developed using the Samsung Galaxy S with the Android OS, as well as open source–based software and other hardware. It is easy to install, control remotely, and monitor the status of imaging devices. We assessed the accuracy of the micro-electro-mechanical system (MEMS) sensors of the smartphone to evaluate the applicability of our environmental monitoring system. The assessment was conducted via survey using metric cameras, a global positioning system receiver, a three-dimensional laser scanner and total station, geometric correction, and digital elevation models generated with camera internal elements, external elements, and ground control points. We demonstrated the effectiveness of the system, and showed that the accuracy of the MEMS sensor and camera calibration have a significant effect on image analysis.