ArticleScientific reports2021
Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- Deep learning differentiates myeloproliferative neoplasms and predicts JAK2 and CALR mutations from bone marrow smears.Annals of hematology · 2026Article
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears.Communications medicine · 2026Article
- Sequential Analysis of Murine Myelofibrosis Models Using a Novel Deep Learning-Based Fibrosis Quantitative Method.EJHaem · 2026Article
- Artificial Intelligence in Haematologic Diagnostics: Current Applications and Future Perspectives.Acta haematologica · 2026Review
- 14,441 Genomics-Based Validated Automated Comprehensive Clinicopathologic Correlations for Myeloid Neoplasms.Scientific data · 2025Article
- Artificial intelligence-based quantitative bone marrow pathology analysis for myeloproliferative neoplasms.Haematologica · 2025Article
- Interpretable Multiple Instance Learning for Hematologic Diagnosis from Peripheral Blood Smears.Research square · 2025Article
- A Dual-Feature Framework for Enhanced Diagnosis of Myeloproliferative Neoplasm Subtypes Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Article
- Review
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Applications of artificial intelligence to myeloproliferative neoplasms: a narrative review.Expert review of hematology · 2024Review
- Domain affiliated distilled knowledge transfer for improved convergence of Ph-negative MPN identifier.PloS one · 2024Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
12 authors.
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Abstract
Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.
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