Enzyme-linked immunosorbent assay (ELISA) in a microplate format has been a gold standard first-line clinical test for diagnosis of various diseases including infectious diseases. However, this technology requires a relatively large and expensive multi-well scanning spectrophotometer to read and quantify the signal from each well, hindering its implementation in resource-limited-settings. Here, we demonstrate a cost-effective and handheld smartphone-based colorimetric microplate reader for rapid digitization and quantification of immunoserology-related ELISA tests in a conventional 96-well plate format at the point of care (POC). This device consists of a bundle of 96 optical fibers to collect the transmitted light from each well of the microplate and direct all the transmission signals from the wells onto the camera of the mobile-phone. Captured images are then transmitted to a remote server through a custom-designed app, and both quantitative and qualitative diagnostic results are returned back to the user within ~1 minute per 96-well plate by using a machine learning algorithm. We tested this mobile-phone based micro-plate reader in a clinical microbiology lab using FDA-approved mumps IgG, measles IgG, and herpes simplex virus IgG (HSV-1 and HSV-2) ELISA tests on 1138 remnant patient samples (roughly 50% training and 50% testing), and achieved an overall accuracy of ~99% or higher for each ELISA test. This handheld and cost-effective platform could be immediately useful for large-scale vaccination monitoring in low-infrastructure settings, and also for other high-throughput disease screening applications at POC.
Quantification of plant health is crucial for agriculture and can even be used to monitor environmental factors and climate related changes. Plant health is known to be directly related to the chlorophyll content of leaves, which correlates with the capacity of the leaves to transmit or absorb light. The gold-standard method for measuring the chlorophyll concentration of a leaf is based on chemical extraction, which is complex, destructive and time-consuming. As an alternative, here we present a field-portable, cost-effective, and colorimetric method to quantify the chlorophyll content of leaves using Google Glass. For this purpose, we created a custom designed handheld device which is battery-powered and 3D-printed to separately provide uniform illumination of a selected region of interest on the leaf surface using red and white light-emitting-diodes (LEDs). The design of this device minimizes the interference of ambient light conditions to our chlorophyll measurements performed through the Glass camera. We tested this platform by using fifteen randomly selected plant species from UCLA Botanical Garden and imaging fully-grown leaves of these species using Glass. An image-processing algorithm was developed to process the acquired images and obtain the chlorophyll concentration information using the red channel intensities in our region-of-interest for both the white and red LED illumination conditions. The results obtained by this algorithm are in good agreement with the SPAD indices measured for each plant, demonstrating that Google Glass, in combination with our custom-designed illumination platform, can expand its functionality to be used as a chlorophyll meter in field-settings.
Integration of optical imagers and sensors into recently emerging wearable computational devices allows for simpler and more intuitive methods of integrating biomedical imaging and medical diagnostics tasks into existing infrastructures. Here we demonstrate the ability of one such device, the Google Glass, to perform qualitative and quantitative analysis of immunochromatographic rapid diagnostic tests (RDTs) using a voice-commandable hands-free software-only interface, as an alternative to larger and more bulky desktop or handheld units. Using the built-in camera of Glass to image one or more RDTs (labeled with Quick Response (QR) codes), our Glass software application uploads the captured image and related information (e.g., user name, GPS, etc.) to our servers for remote analysis and storage. After digital analysis of the RDT images, the results are transmitted back to the originating Glass device, and made available through a website in geospatial and tabular representations. We tested this system on qualitative human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) RDTs. For qualitative HIV tests, we demonstrate successful detection and labeling (i.e., yes/no decisions) for up to 6-fold dilution of HIV samples. For quantitative measurements, we activated and imaged PSA concentrations ranging from 0 to 200 ng/mL and generated calibration curves relating the RDT line intensity values to PSA concentration. By providing automated digitization of both qualitative and quantitative test results, this wearable colorimetric diagnostic test reader platform on Google Glass can reduce operator errors caused by poor training, provide real-time spatiotemporal mapping of test results, and assist with remote monitoring of various biomedical conditions.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.