ArticleBlood2021
Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set.
Article in Blood, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 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
- An interpretable deep learning framework for multiclass bone marrow cytomorphology classification using EfficientNet and post hoc visualization techniques.Journal of pathology informatics · 2026Article
- Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges.Blood research · 2026Review
- Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.International journal of laboratory hematology · 2026Review
- Deep Learning Classification of Uveal Melanoma and Metastatic Carcinoma on Cytology Specimens: A Single-Center Experience.Investigative ophthalmology & visual science · 2026Article
- An AI-powered self-driving microscope for low-cost acute leukemia detection.Nature communications · 2026Article
- XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- An expert-level vision-language model for multitask diagnostic morphology in clinical laboratories.NPJ digital medicine · 2026Article
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 2026Review
- AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.Leukemia · 2026Article
- Multicentric data challenge for artificial intelligence-based classification of leukocytes: results from the CytologIA consortium.NPJ precision oncology · 2026Article
- Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears.Communications medicine · 2026Article
- Cross-platform deep learning enables automated cytomorphologic subtyping of acute leukemia from bone marrow smears.Virchows Archiv : an international journal of pathology · 2026Article
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- International testing and refinement of AI algorithms predicting acute leukemia subtypes from routine laboratory data.Nature communications · 2026Article
- Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples.Scientific reports · 2026Article
- Review
22 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.
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