High-throughput, single-cell optofluidic platform for point-of-care diagnosis

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Two breakthrough technologies are generated with the completion of this project. The first one is intelligent platelet aggregate classifier (iPAC) that performs high-throughput imaging and intelligent analysis of platelet aggregates produced by different agonists and classifies platelet aggregates by agonist type. Our finding that platelet aggregates can be classified by agonist type through their morphology is unprecedented as it has never been reported previously. The information about the driving factors behind the formation of platelet aggregates is expected to lead to a better understanding of the underlying mechanism of platelet aggregation and open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics. The second one is the virtual optofluidic time-stretch quantitative phase imaging, which generates “virtual” phase images from their corresponding bright-field images by using a deep neural network trained with numerous pairs of bright-field and phase images and identifies cancer cells from white blood cells based on their bright-field and virtual phase images. The fellow, Dr Yan, has presented his work in a prestigious international conference (Australia and New Zealand Nanofluidics and Microfluidics conference). Meanwhile, 3 manuscripts are generated from this project.