ArticleProceedings of the National Academy of Sciences of the United States of America2020
Objective assessment of stored blood quality by deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Evaluating Isochoric Freezing as a Strategy for Storage of Red Blood Cells.Biotechnology and bioengineering · 2026Article
- Microfluidic Chip Platforms for Red Blood Cell Storage Lesion Quality Control and Precision Transfusion.Research (Washington, D.C.) · 2026Review
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques.Journal of biophotonics · 2025Review
- Pathophysiological Associations and Measurement Techniques of Red Blood Cell Deformability.Biosensors · 2025Review
- Unbiased Morphometric Assessment of Red Blood Cell Storage Lesion in the Presence of Shear-Induced Stomatocytes.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2025Article
- Engineered supercooling systems for enhanced long-term preservation of large-volume red blood cells in commercial blood bags.Journal of biological engineering · 2025Article
- Cold storage surpasses the impact of biological age and donor characteristics on red blood cell morphology classified by deep machine learning.Scientific reports · 2025Article
- Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.Annals of hematology · 2025Article
- Imaging flow cytometry as a novel approach for the diagnosis of heparin-induced thrombocytopenia.British journal of haematology · 2025Article
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
- Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis.Research (Washington, D.C.) · 2025Article
- Review
- New and emerging technologies for pretransfusion blood quality assessment: A state-of-the-art review.Transfusion · 2024Review
- Biophysical profiling of red blood cells from thin-film blood smears using deep learning.Heliyon · 2024Article
- Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia.International journal of molecular sciences · 2024Article
- Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data.Scientific reports · 2024Article
- Reply to Kaestner et al.: Pioneering quantitative platforms for stored red blood cell assessment open the door for precision transfusion medicine.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
19 authors.
Funding
Abstract
Stored red blood cells (RBCs) are needed for life-saving blood transfusions, but they undergo continuous degradation. RBC storage lesions are often assessed by microscopic examination or biochemical and biophysical assays, which are complex, time-consuming, and destructive to fragile cells. Here we demonstrate the use of label-free imaging flow cytometry and deep learning to characterize RBC lesions. Using brightfield images, a trained neural network achieved 76.7% agreement with experts in classifying seven clinically relevant RBC morphologies associated with storage lesions, comparable to 82.5% agreement between different experts. Given that human observation and classification may not optimally discern RBC quality, we went further and eliminated subjective human annotation in the training step by training a weakly supervised neural network using only storage duration times. The feature space extracted by this network revealed a chronological progression of morphological changes that better predicted blood quality, as measured by physiological hemolytic assay readouts, than the conventional expert-assessed morphology classification system. With further training and clinical testing across multiple sites, protocols, and instruments, deep learning and label-free imaging flow cytometry might be used to routinely and objectively assess RBC storage lesions. This would automate a complex protocol, minimize laboratory sample handling and preparation, and reduce the impact of procedural errors and discrepancies between facilities and blood donors. The chronology-based machine-learning approach may also improve upon humans' assessment of morphological changes in other biomedically important progressions, such as differentiation and metastasis.
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