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...

Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.

Analyzing electrolytes in urine, such as sodium, potassium, calcium, chloride, and nitrite, has significant diagnostic value in detecting various conditions, such as kidney disorder, urinary stone disease, urinary tract infection, and cystic fibrosis. Ideally, by regularly monitoring these ions with the convenience of dipsticks and portable tools, such as cellphones, informed decision making is possible to control the consumption of these ions. Here, we report a paper-based sensor for measuring the concentration of sodium, potassium, calcium, chloride, and nitrite in urine, accurately quantified using a smartphone-enabled platform. By testing the device with both Tris buffer and artificial urine containing a wide range of electrolyte concentrations, we demonstrate that the proposed device can be used for detecting potassium, calcium, chloride, and nitrite within the whole physiological range of concentrations, and for binary quantification of sodium concentration.

Nosema ceranae detection using a mobile phone.