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Sadafi, A. ; Bordukova, M. ; Makhro, A.* ; Navab, N.* ; Bogdanova, A.* ; Marr, C.

RedTell: An AI tool for interpretable analysis of red blood cell morphology.

Front. Physiol. 14:1058720 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold
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Introduction: Hematologists analyze microscopic images of red blood cells to study their morphology and functionality, detect disorders and search for drugs. However, accurate analysis of a large number of red blood cells needs automated computational approaches that rely on annotated datasets, expensive computational resources, and computer science expertise. We introduce RedTell, an AI tool for the interpretable analysis of red blood cell morphology comprising four single-cell modules: segmentation, feature extraction, assistance in data annotation, and classification. Methods: Cell segmentation is performed by a trained Mask R-CNN working robustly on a wide range of datasets requiring no or minimum fine-tuning. Over 130 features that are regularly used in research are extracted for every detected red blood cell. If required, users can train task-specific, highly accurate decision tree-based classifiers to categorize cells, requiring a minimal number of annotations and providing interpretable feature importance. Results: We demonstrate RedTell's applicability and power in three case studies. In the first case study we analyze the difference of the extracted features between the cells coming from patients suffering from different diseases, in the second study we use RedTell to analyze the control samples and use the extracted features to classify cells into echinocytes, discocytes and stomatocytes and finally in the last use case we distinguish sickle cells in sickle cell disease patients. Discussion: We believe that RedTell can accelerate and standardize red blood cell research and help gain new insights into mechanisms, diagnosis, and treatment of red blood cell associated disorders.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Classification ; Deep Learning ; Interpretable Machine Learning ; Microscopic Image Analysis ; Morphological Features Extraction ; Segmentation ; Single Red Blood Cells ; Vesicle Detection; Erythrocyte Classification; Calcium; Channels; System; Shape
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 1664-042X
e-ISSN 1664-042X
Journal Frontiers in Physiology
Quellenangaben Volume: 14, Issue: , Pages: , Article Number: 1058720 Supplement: ,
Publisher Frontiers
Publishing Place Lausanne
Reviewing status Peer reviewed
Institute(s) Human-Centered AI (HCA)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540007-001
Grants European Research Council (ERC)
European Research Council (ERC) under the European Union
DOI 10.3389/fphys.2023.1058720
WOS ID 001004425100001
Scopus ID 85161885369
PubMed ID 37304818
Erfassungsdatum 2023-10-06
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