An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays
This provides a scalable computational platform for investigating sickle cell disease biomechanics and assessing therapeutic efficacy, though it is incremental as it applies existing methods to a specific biomedical domain.
The researchers developed an automated deep learning framework to quantify overlapping red blood cell sickling dynamics in microfluidic assays, achieving high segmentation performance with limited labeled data and more than doubling experimental throughput via densely packed cell suspensions.
Understanding sickle cell dynamics requires accurate identification of morphological transitions under diverse biophysical conditions, particularly in densely packed and overlapping cell populations. Here, we present an automated deep learning framework that integrates AI-assisted annotation, segmentation, classification, and instance counting to quantify red blood cell (RBC) populations across varying density regimes in time-lapse microscopy data. Experimental images were annotated using the Roboflow platform to generate labeled dataset for training an nnU-Net segmentation model. The trained network enables prediction of the temporal evolution of the sickle cell fraction, while a watershed algorithm resolves overlapping cells to enhance quantification accuracy. Despite requiring only a limited amount of labeled data for training, the framework achieves high segmentation performance, effectively addressing challenges associated with scarce manual annotations and cell overlap. By quantitatively tracking dynamic changes in RBC morphology, this approach can more than double the experimental throughput via densely packed cell suspensions, capture drug-dependent sickling behavior, and reveal distinct mechanobiological signatures of cellular morphological evolution. Overall, this AI-driven framework establishes a scalable and reproducible computational platform for investigating cellular biomechanics and assessing therapeutic efficacy in microphysiological systems.