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

2026-01-25

AI Summary

Accurate identification of sickled red blood cells in densely packed and overlapping configurations under varying biophysical conditions remains highly challenging, particularly due to the scarcity of high-quality manual annotations. To address this, this work proposes a deep learning–based automated analysis framework that integrates AI-assisted annotation, nnU-Net segmentation, watershed-based post-processing, and instance-level classification and counting. Requiring only minimal expert-labeled data, the method achieves high-precision quantification of dynamic sickling processes in microfluidic experiments. It significantly enhances experimental throughput by more than twofold and enables accurate temporal prediction of sickling ratios, thereby effectively uncovering drug-dependent sickling behaviors and associated cellular mechanical properties. This approach provides a powerful and efficient tool for both therapeutic evaluation and fundamental research into sickle cell disease.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• sickle cell dynamics
• overlapping red blood cells
• morphological transitions
• microfluidic assays
• cell segmentation

Innovation

• deep learning
• red blood cell segmentation
• cell overlap resolution
• microfluidic assay
• sickle cell dynamics