Machine learning (ML) can be an appropriate approach to overcoming common problems associated with sensors for low-cost, point-of-care diagnostics, such as non-linearity, multidimensionality, sensor-to-sensor variations, presence of anomalies, and ambiguity in key features. This study proposes a novel approach based on ML algorithms (neural nets, Gaussian Process Regression, among others) to model the electrochemiluminescence (ECL) quenching mechanism of the [Ru(bpy)3]2+/TPrA system by phenolic compounds, thus allowing their detection and quantification. The relationships between the concentration of phenolic compounds and their effect on the ECL intensity and current data measured using a mobile phone-based ECL sensor is investigated. The ML regression tasks with a tri-layer neural net using minimally processed time series data showed better or comparable detection performance compared to the performance using extracted key features without extra preprocessing. Combined multimodal characteristics produced an 80% more enhanced performance with multilayer neural net algorithms than a single feature based-regression analysis. The results demonstrated that the ML could provide a robust analysis framework for sensor data with noises and variability. It demonstrates that ML strategies can play a crucial role in chemical or biosensor data analysis, providing a robust model by maximizing all the obtained information and integrating nonlinearity and sensor-to-sensor variations.

Contrast-enhanced ultrasound (CEUS) continues to be an ever-growing tool in radiation-free imaging. While it has been widely used in cardiac imaging, CEUS has only recently become an Food and Drug Administration-approved and viable modality for evaluation of abdominal structures. Ultrasound contrast agents are nontoxic, microbubble-based vascular agents and can be used to reliably assess enhancement patterns of various lesions in real time. In particular, it's non nephrotoxic nature makes CEUS a particularly important tool in renal failure patients requiring serial follow-up. This review provides a comprehensive discussion on the utility of CEUS agents, imaging techniques, comparison with traditional cross-sectional imaging modalities, and its application in diagnosing kidney and liver lesions. This pictorial review is illustrated with cases of renal and hepatic lesions that the practicing radiologist should become familiar with as CEUS becomes increasingly popular.

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Over the past decade machine learning and artificial intelligence's resurgence spawned the desire to mimic human creative ability. Initially attempts to create images, music, and text flooded the community, though little has been learned regarding constrained, one-dimensional data generation. This paper demonstrates a variational autoencoder approach to this problem. By modeling biosensor current and concentration data we aim to augment the existing dataset. In training a multi-layer neural network based encoder and decoder we were able to generate realistic, original samples., These results demonstrate the ability to realistically augment datasets, improving training of machine learning models designed to predict concentration from input signals.