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Related Experiment Video

Updated: May 12, 2026

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
10:27

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

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

Nikhil Kadivar1, Guansheng Li2, Jianlu Zheng3

  • 1School of Engineering, Brown University, Providence, RI, USA. george_karniadakis@brown.edu.

Lab on a Chip
|May 11, 2026
PubMed
Summary

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An AI deep learning framework automates red blood cell (RBC) analysis in dense suspensions, improving sickle cell disease research. This method enhances experimental throughput and drug efficacy assessment in microfluidic systems.

Area of Science:

  • Biophysics
  • Computational Biology
  • Hematology

Background:

  • Accurate quantification of sickle cell dynamics is crucial but challenging in dense cell populations due to overlap and aggregation.
  • Traditional methods like dilution reduce statistical power and do not eliminate clustering in microfluidic sickling assays.
  • Longitudinal studies require tracking large cell populations to capture cumulative, history-dependent changes.

Purpose of the Study:

  • To develop an automated deep learning framework for quantifying red blood cell (RBC) populations in dense suspensions.
  • To enable robust analysis of RBC morphological transitions under various biophysical conditions, overcoming limitations of cell overlap.
  • To establish a scalable computational platform for investigating cellular biomechanics and assessing therapeutic efficacy.

More Related Videos

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

Related Experiment Videos

Last Updated: May 12, 2026

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation
10:27

Controlled Microfluidic Environment for Dynamic Investigation of Red Blood Cell Aggregation

Published on: June 4, 2015

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
09:12

Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry

Published on: January 12, 2018

Main Methods:

  • Utilized AI-assisted annotation (Roboflow) and deep learning (nnU-Net) for segmentation and classification of RBCs in time-lapse microscopy.
  • Integrated a watershed algorithm to separate overlapping cells, enhancing quantification accuracy in dense suspensions.
  • Trained the model with limited labeled data, demonstrating high segmentation performance and addressing scarcity of manual annotations.

Main Results:

  • The AI framework accurately quantifies RBC populations across varying densities, predicting temporal evolution of the sickle cell fraction.
  • Achieved high segmentation performance, effectively handling challenges of cell overlap and limited manual annotations.
  • Demonstrated potential to more than double experimental throughput in dense cell suspensions and capture drug-dependent sickling behavior.

Conclusions:

  • The AI-driven framework provides a scalable and reproducible computational platform for investigating RBC biomechanics.
  • This approach enables quantitative tracking of dynamic changes in RBC morphology, revealing distinct mechanobiological signatures.
  • Facilitates more efficient assessment of therapeutic efficacy in microphysiological systems for sickle cell disease research.