ArticleCommunications medicine2022
Automated bone marrow cytology using deep learning to generate a histogram of cell types.
Article in Communications medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 108 citations in OpenAlex.
- Whole-slide imaging in hematopathology: Current state, challenges and future opportunities.Journal of pathology informatics · 2026Review
- Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges.Blood research · 2026Review
- An AI-powered self-driving microscope for low-cost acute leukemia detection.Nature communications · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Multicentric data challenge for artificial intelligence-based classification of leukocytes: results from the CytologIA consortium.NPJ precision oncology · 2026Article
- MurineCyto-Det: A High-Resolution Murine BALF Cytology Dataset for Leukocyte Segmentation and Detection.bioRxiv : the preprint server for biology · 2026Article
- Interpretative diagnostic model for neuroblastoma metastases using bone marrow cytology.Journal of translational medicine · 2026Article
- Article
- Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples.Scientific reports · 2026Article
- Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.Frontiers in medicine · 2026Article
- Smart FL: meta-learning for federated blood marrow smear classification.Frontiers in oncology · 2026Article
- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- AI-based detection of neutrophil dysplasia: an accessible and sensitive model for MDS diagnosis from peripheral blood.Annals of hematology · 2025Article
- PredictingiScience · 2025Article
- Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images.Scientific reports · 2025Article
- Annotation-free deep learning for predicting gene mutations from whole slide images of acute myeloid leukemia.NPJ precision oncology · 2025Article
- VFM-SSL-BMADCC-Framework: vision foundation model and self-supervised learning based automated framework for differential cell counts on whole-slide bone marrow aspirate smears.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. Methods: We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint. Results: Our system achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mean average precision, 0.78 average F1-score, Log-average miss rate of 0.31). Conclusions: HCT has potential to eventually support more efficient and accurate diagnosis in hematology, supporting AI-enabled computational pathology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.