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

TL;DR

This paper introduces an AI-powered deep learning framework that automates the analysis of red blood cell sickling dynamics in microfluidic assays, improving accuracy and throughput in densely packed cell populations.

Contribution

The study presents a novel integrated AI-based platform combining annotation, segmentation, classification, and counting to analyze RBC morphology and dynamics in challenging dense cell environments.

Findings

High segmentation accuracy with limited labeled data

Doubles experimental throughput in dense cell suspensions

Reveals drug effects and mechanobiological signatures

Abstract

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.