Air quality monitoring using mobile microscopy and machine learning.

Rapid, accurate and high-throughput sizing and quantification of particulate matter (PM) in air is crucial for monitoring and improving air quality. In fact, particles in air with a diameter of ≤2.5 μm have been classified as carcinogenic by the World Health Organization.

Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes.

Purpose: To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast.

Methods: We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm.

High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader.

Routine antimicrobial susceptibility testing (AST) can prevent deaths due to bacteria and reduce the spread of multi-drug-resistance, but cannot be regularly performed in resource-limited-settings due to technological challenges, high-costs, and lack of trained professionals. We demonstrate an automated and cost-effective cellphone-based 96-well microtiter-plate (MTP) reader, capable of performing AST without the need for trained diagnosticians. Our system includes a 3D-printed smartphone attachment that holds and illuminates the MTP using a light-emitting-diode array.

Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays.

Standard microplate based enzyme-linked immunosorbent assays (ELISA) are widely utilized for various nanomedicine, molecular sensing, and disease screening applications, and this multiwell plate batched analysis dramatically reduces diagnosis costs per patient compared to nonbatched or nonstandard tests. However, their use in resource-limited and field-settings is inhibited by the necessity for relatively large and expensive readout instruments. To mitigate this problem, we created a hand-held and cost-effective cellphone-based colorimetric microplate reader, which uses a 3D-printed opto-mechanical attachment to hold and illuminate a 96-well plate using a light-emitting-diode (LED) array.

Quantification of plant chlorophyll content using Google Glass.

Measuring plant chlorophyll concentration is a well-known and commonly used method in agriculture and environmental applications for monitoring plant health, which also correlates with many other plant parameters including, e.g., carotenoids, nitrogen, maximum green fluorescence, etc.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes.

Imaging and sizing of single DNA molecules on a mobile phone.

DNA imaging techniques using optical microscopy have found numerous applications in biology, chemistry and physics and are based on relatively expensive, bulky and complicated set-ups that limit their use to advanced laboratory settings. Here we demonstrate imaging and length quantification of single molecule DNA strands using a compact, lightweight and cost-effective fluorescence microscope installed on a mobile phone. In addition to an optomechanical attachment that creates a high contrast dark-field imaging setup using an external lens, thin-film interference filters, a miniature dovetail stage and a laser-diode for oblique-angle excitation, we also created a computational framework and a mobile phone application connected to a server back-end for measurement of the lengths of individual DNA molecules that are labeled and stretched using disposable chips.

Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay.

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99% of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed.

X-ray scatter correction in breast tomosynthesis with a precomputed scatter map library.

Purpose: To develop and evaluate the impact on lesion conspicuity of a software-based x-ray scatter correction algorithm for digital breast tomosynthesis (DBT) imaging into which a precomputed library of x-ray scatter maps is incorporated.

Methods: A previously developed model of compressed breast shapes undergoing mammography based on principal component analysis (PCA) was used to assemble 540 simulated breast volumes, of different shapes and sizes, undergoing DBT. A Monte Carlo (MC) simulation was used to generate the cranio-caudal (CC) view DBT x-ray scatter maps of these volumes, which were then assembled into a library.

Immunochromatographic diagnostic test analysis using Google Glass.

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.

Detection and spatial mapping of mercury contamination in water samples using a smart-phone.

Detection of environmental contamination such as trace-level toxic heavy metal ions mostly relies on bulky and costly analytical instruments. However, a considerable global need exists for portable, rapid, specific, sensitive, and cost-effective detection techniques that can be used in resource-limited and field settings. Here we introduce a smart-phone-based hand-held platform that allows the quantification of mercury(II) ions in water samples with parts per billion (ppb) level of sensitivity.

BioGames: A Platform for Crowd-Sourced Biomedical Image Analysis and Telediagnosis.

We have recently proposed a mathematical framework for crowd-sourcing of biomedical image analysis and diagnosis through digital gaming. Here we review our recent progress on this gaming platform and demonstrate its viability for telediagnosis of malaria, achieving an accuracy that is within less than 2 percent of that of a trained expert.

Objective models of compressed breast shapes undergoing mammography.

Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes.

Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables.

A mathematical framework for combining decisions of multiple experts toward accurate and remote diagnosis of malaria using tele-microscopy.

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.

Crowd-sourced BioGames: managing the big data problem for next-generation lab-on-a-chip platforms.

We describe a crowd-sourcing based solution for handling large quantities of data that are created by e.g., emerging digital imaging and sensing devices, including next generation lab-on-a-chip platforms.

Characterization of the homogeneous tissue mixture approximation in breast imaging dosimetry.

Purpose: To compare the estimate of normalized glandular dose in mammography and breast CT imaging obtained using the actual glandular tissue distribution in the breast to that obtained using the homogeneous tissue mixture approximation.

Methods: Twenty volumetric images of patient breasts were acquired with a dedicated breast CT prototype system and the voxels in the breast CT images were automatically classified into skin, adipose, and glandular tissue. The breasts in the classified images underwent simulated mechanical compression to mimic the conditions present during mammographic acquisition.

Distributed medical image analysis and diagnosis through crowd-sourced games: a malaria case study.

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g.

Clinical digital breast tomosynthesis system: dosimetric characterization.

Purpose: To comprehensively characterize the dosimetric properties of a clinical digital breast tomosynthesis (DBT) system for the acquisition of mammographic and tomosynthesis images.

Materials And Methods: Compressible water-oil mixture phantoms were created and imaged by using the automatic exposure control (AEC) of the Selenia Dimensions system (Hologic, Bedford, Mass) in both DBT and full-field digital mammography (FFDM) mode. Empirical measurements of the x-ray tube output were performed with a dosimeter to measure the air kerma for the range of tube current-exposure time product settings and to develop models of the automatically selected x-ray spectra.

Lens-free optical tomographic microscope with a large imaging volume on a chip.

We present a lens-free optical tomographic microscope, which enables imaging a large volume of approximately 15 mm(3) on a chip, with a spatial resolution of < 1 μm × < 1 μm × < 3 μm in x, y and z dimensions, respectively. In this lens-free tomography modality, the sample is placed directly on a digital sensor array with, e.g.

Dosimetric characterization of a dedicated breast computed tomography clinical prototype.

Purpose: To investigate the glandular dose magnitudes and characteristics resulting from image acquisition using a dedicated breast computed tomography (BCT) clinical prototype imaging system.

Methods: The x-ray spectrum and output characteristics of a BCT clinical prototype (Koning Corporation, West Henrietta, NY) were determined using empirical measurements, breast phantoms, and an established spectrum model. The geometry of the BCT system was replicated in a Monte Carlo-based computer simulation using the GEANT4 toolkit and was validated by comparing the simulated results for exposure distribution in a standard 16 cm CT head phantom with those empirically determined using a 10 cm CT pencil ionization chamber and dosimeter.

An analysis of glutamate spillover on the N-methyl-D-aspartate receptors at the cerebellar glomerulus.

Glutamate spillover is thought to play a significant role in increasing neural transmission at the mossy fiber/granule cell cerebellar glomerulus. Glutamate spillover has been shown to activate AMPA receptors at the glomerulus, and here we complete the characterization of spillover at the glomerulus by investigating the role of glutamate spillover in N-methyl-D-aspartate receptor (NMDA-R) activation. We present a quantitative model of glutamate spillover combining recent models of glutamate diffusion and NMDA-R binding to determine the open probabilities of NMDA-Rs over time at a neighbor synapse.