Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics

TL;DR

This paper introduces RT-HAD, a deep learning framework for real-time blood cell aggregate detection using holographic microscopy, improving diagnostics by enabling rapid, accurate, label-free analysis of blood samples.

Contribution

The authors develop RT-HAD, a novel end-to-end deep learning system that processes large holographic microscopy data in real-time for blood cell aggregate detection.

Findings

Processes over 30 GB of data in less than 1.5 minutes

Achieves an 8.9% error rate in platelet aggregate detection

Matches laboratory error rates for haematology biomarkers

Abstract

While analysing rare blood cell aggregates remains challenging in automated haematology, they could markedly advance label-free functional diagnostics. Conventional flow cytometers efficiently perform cell counting with leukocyte differentials but fail to identify aggregates with flagged results, requiring manual reviews. Quantitative phase imaging flow cytometry captures detailed aggregate morphologies, but clinical use is hampered by massive data storage and offline processing. Incorporating hidden biomarkers into routine haematology panels would significantly improve diagnostics without flagged results. We present RT-HAD, an end-to-end deep learning-based image and data processing framework for off-axis digital holographic microscopy (DHM), which combines physics-consistent holographic reconstruction and detection, representing each blood cell in a graph to recognize aggregates. RT-HAD processes >30 GB of image data on-the-fly with turnaround time of <1.5 min and error rate of 8.9% in platelet aggregate detection, which matches acceptable laboratory error rates of haematology biomarkers and solves the big data challenge for point-of-care diagnostics.