Fluorescent microscopy and flow cytometry are widely used tools in biomedical sciences. Cost-effective translation of these technologies to remote and resource-limited environments could create new opportunities especially for telemedicine applications. Toward this direction, here we demonstrate the integration of imaging cytometry and fluorescent microscopy on a cell phone using a compact, lightweight, and cost-effective optofluidic attachment. In this cell-phone-based optofluidic imaging cytometry platform, fluorescently labeled particles or cells of interest are continuously delivered to our imaging volume through a disposable microfluidic channel that is positioned above the existing camera unit of the cell phone. The same microfluidic device also acts as a multilayered optofluidic waveguide and efficiently guides our excitation light, which is butt-coupled from the side facets of our microfluidic channel using inexpensive light-emitting diodes. Since the excitation of the sample volume occurs through guided waves that propagate perpendicular to the detection path, our cell-phone camera can record fluorescent movies of the specimens as they are flowing through the microchannel. The digital frames of these fluorescent movies are then rapidly processed to quantify the count and the density of the labeled particles/cells within the target solution of interest. We tested the performance of our cell-phone-based imaging cytometer by measuring the density of white blood cells in human blood samples, which provided a decent match to a commercially available hematology analyzer. We further characterized the imaging quality of the same platform to demonstrate a spatial resolution of ~2 μm. This cell-phone-enabled optofluidic imaging flow cytometer could especially be useful for rapid and sensitive imaging of bodily fluids for conducting various cell counts (e.g., toward monitoring of HIV+ patients) or rare cell analysis as well as for screening of water quality in remote and resource-poor settings.

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., ≤4 mm distance to its active area. A partially coherent light source placed approximately 70 mm away from the sensor is employed to record lens-free in-line holograms of the sample from different viewing angles. At each illumination angle, multiple subpixel shifted holograms are also recorded, which are digitally processed using a pixel superresolution technique to create a single high-resolution hologram of each angular projection of the object. These superresolved holograms are digitally reconstructed for an angular range of AE50°, which are then back-projected to compute tomograms of the sample. In order to minimize the artifacts due to limited angular range of tilted illumination, a dual-axis tomography scheme is adopted, where the light source is rotated along two orthogonal axes. Tomographic imaging performance is quantified using microbeads of different dimensions, as well as by imaging wild-type Caenorhabditis elegans. Probing a large volume with a decent 3D spatial resolution, this lens-free optical tomography platform on a chip could provide a powerful tool for high-throughput imaging applications in, e.g., cell and developmental biology. L ight microscopy has been an irreplaceable tool in life sciences for several centuries. The quest to resolve smaller features with better resolution and contrast has improved the capabilities of this important tool at the cost of relatively increasing its size and complexity (1). On the other hand, we have experienced the flourishing of emerging technologies such as microfluidic and lab-on-a-chip systems, which offer fast and efficient handling and processing of biological samples within highly miniaturized architectures (2-7). The optical inspection of the specimen, however, is still being performed by conventional light microscopes, which has in general several orders of magnitude size mismatch compared to the scale of the microfluidic systems. As a result, there is a clear need for alternative compact microscopy modalities toward integration with miniaturized lab-on-a-chip platforms (8).The push for new optical microscopy modalities is not solely driven by the need for miniaturization and microfluidic integration. The fact that high resolution is achieved at the cost of significant field-of-view (FOV) reduction is another fundamental limitation of lens-based imaging. The relatively small FOV of conventional light microscopy brings additional challenges for its application to several important problems such as rare cell imaging or optical phenotyping of model organisms (9-15), where high-throughput microscopy is highly desired.In order to provide complementary solutions to these aforementioned needs, several lens-free digital microscopy techniqu...

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., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.

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. We show that in cases where the diagnosis is a binary decision (e.g., positive vs. negative, or infected vs. uninfected), it is possible to make accurate diagnosis by crowd-sourcing the raw data (e.g., microscopic images of specimens/cells) using entertaining digital games (i.e., BioGames) that are played on PCs, tablets or mobile phones. We report the results and the analysis of a large-scale public BioGames experiment toward diagnosis of malaria infected human red blood cells (RBCs), where binary responses from approximately 1000 untrained individuals from more than 60 different countries are combined together (corresponding to more than 1 million cell diagnoses), resulting in an accuracy level that is comparable to those of expert medical professionals. This BioGames platform holds promise toward cost-effective and accurate tele-pathology, improved training of medical personnel, and can also be used to manage the “Big Data” problem that is emerging through next generation digital lab-on-a-chip devices.