ArticleBlood advances2020
Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients.
Article in Blood advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Megakaryocytes in myelofibrosis: mechanisms of fibrotic niche remodeling and therapeutic implications.Biomarker research · 2026Review
- Deep learning differentiates myeloproliferative neoplasms and predicts JAK2 and CALR mutations from bone marrow smears.Annals of hematology · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
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- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.Haematologica · 2025Article
- The utility of automated artificial intelligence-assisted digital cytomorphology for bone marrow analysis in diagnostic haemato-oncology.Clinical and translational medicine · 2025Article
- A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Article
- Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.British journal of haematology · 2025Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Predicting modelling for early diagnosis in early myeloproliferative neoplasms, not otherwise specified. Evidence from a machine learning study.Frontiers in oncology · 2025Article
- Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome.Bioengineering (Basel, Switzerland) · 2024Article
- A proinflammatory stem cell niche drives myelofibrosis through a targetable galectin-1 axis.Science translational medicine · 2024Article
- Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review.Expert review of hematology · 2024Review
- CHIP-JAK2V617F, chronic inflammation, abnormal megakaryocyte morphology, organ failure, and multimorbidities.Blood advances · 2024Article
- Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.Frontiers in medicine · 2024Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established diagnoses of essential thrombocythemia (n = 45), polycythemia vera (n = 18), or myelofibrosis (n = 25). We describe the application of an automated workflow for the identification and delineation of relevant histological features from routinely prepared BMTs. Subsequent analysis enabled the tissue diagnosis of MPN with a high predictive accuracy (area under the curve = 0.95) and revealed clear evidence of the potential to discriminate between important MPN subtypes. Our method of visually representing abstracted megakaryocyte features in the context of analyzed patient cohorts facilitates the interpretation and monitoring of samples in a manner that is beyond conventional approaches. The automated BMT phenotyping approach described here has significant potential as an adjunct to standard genetic and molecular testing in established or suspected MPN patients, either as part of the routine diagnostic pathway or in the assessment of disease progression/response to treatment.
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