Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Delikoyun, Kerem Chen, Qianyu Wei, Liu Myo, Si Ko Krell, Johannes Schlegel, Martin Kuan, Win Sen Soong, John Tshon Yit Schneider, Gerhard da Costa, Clarissa Prazeres Knolle, Percy A. Renia, Laurent Cove, Matthew Edward Lee, Hwee Kuan Diepold, Klaus Hayden, Oliver |
| author_facet | Delikoyun, Kerem Chen, Qianyu Wei, Liu Myo, Si Ko Krell, Johannes Schlegel, Martin Kuan, Win Sen Soong, John Tshon Yit Schneider, Gerhard da Costa, Clarissa Prazeres Knolle, Percy A. Renia, Laurent Cove, Matthew Edward Lee, Hwee Kuan Diepold, Klaus Hayden, Oliver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics Delikoyun, Kerem Chen, Qianyu Wei, Liu Myo, Si Ko Krell, Johannes Schlegel, Martin Kuan, Win Sen Soong, John Tshon Yit Schneider, Gerhard da Costa, Clarissa Prazeres Knolle, Percy A. Renia, Laurent Cove, Matthew Edward Lee, Hwee Kuan Diepold, Klaus Hayden, Oliver Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2508.09215 |