An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.

Understanding sickle cell dynamics requires accurate identification of morphological transitions under diverse biophysical conditions, particularly in densely packed and overlapping cell populations. In microfluidic sickling assays, simple dilution to reduce overlap is often undesirable because it reduces statistical power per experiment, and does not eliminate aggregation-driven clustering under hypoxic conditions. Moreover, longitudinal and cyclic deoxygenation-reoxygenation studies require tracking large cell populations within a single field of view, as all cells in the sample may undergo cumulative history-dependent changes. These experimental constraints necessitate robust quantification directly in dense suspensions. 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 separates 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