Mobile-phones have facilitated the creation of field-portable, cost-effective imaging and sensing technologies that approach laboratory-grade instrument performance. However, the optical imaging interfaces of mobile-phones are not designed for microscopy and produce spatial and spectral distortions in imaging microscopic specimens. Here, we report on the use of deep learning to correct such distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field. After training a convolutional neural network, we successfully imaged various samples, including blood smears, histopathology tissue sections, and parasites, where the recorded images were highly compressed to ease storage and transmission for telemedicine applications. This method is applicable to other low-cost, aberrated imaging systems, and could offer alternatives for costly and bulky microscopes, while also providing a framework for standardization of optical images for clinical and biomedical applications. enhancement and aberration correction were performed computationally using a deep convolutional neural network (see Fig. 1, Supplementary Fig. 1, and the Methods section). Deep learning 12 is a powerful machine learning technique that can perform complex operations using a multi-layered artificial neural network and has shown great success in various tasks for which data are abundant [13][14][15][16] . The use of deep learning has also been demonstrated in numerous biomedical applications, such as diagnosis 17,18 , image classification 19 , among others 20-24 . In our method, a supervised learning approach is first applied by feeding the designed deep network with input (smartphone microscope images) and labels (gold standard benchtop microscope images obtained for the same samples) and optimizing a cost function that guides the network to learn the statistical transformation between the input and label. Following the deep network training phase, the network remains fixed and a smartphone microscope image input into the deep network is rapidly enhanced in terms of spatial resolution, signal-to-noise ratio, and colour response, attempting to match the overall image quality and the field of view (FOV) that would result from using a 20× objective lens on a high-end benchtop microscope. In addition, we demonstrate that the image output by the network will have a larger depth of field (DOF) than the corresponding image acquired using a high-NA objective lens on a benchtop microscope. Each enhanced image of the mobile microscope is inferred by the deep network in a non-iterative, feed-forward manner. For example, the deep network generates an enhanced output image with a FOV of ~0.57 mm 2 (the same as that of a 20× objective lens), from a smartphone microscope image within ~0.42 s, using a standard personal computer equipped with a dual graphics-processing un...
Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.
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. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 μm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 μm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.
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