ArticleScientific reports2023
High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Whole-slide imaging in hematopathology: Current state, challenges and future opportunities.Journal of pathology informatics · 2026Review
- Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.International journal of laboratory hematology · 2026Review
- An AI-powered self-driving microscope for low-cost acute leukemia detection.Nature communications · 2026Article
- Digital Pathology in Hematopathology: From Vision to Deployment.International journal of laboratory hematology · 2026Review
- Consistency analysis of AI cell recognition results between non anticoagulant and anticoagulant marrow smear after Wright's staining.Scientific reports · 2026Article
- A Deep Learning Model for IMMP-Based Residual Disease Monitoring in AML with Monocytic Differentiation.Diagnostics (Basel, Switzerland) · 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
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- AI in hematology: A new frontier for nursing practice and patient care.Journal of family medicine and primary care · 2026Review
- Article
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.Scientific data · 2025Article
- PredictingiScience · 2025Article
- The utility of automated artificial intelligence-assisted digital cytomorphology for bone marrow analysis in diagnostic haemato-oncology.Clinical and translational medicine · 2025Article
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Cell Nuclear Segmentation of B-ALL Images Based on MSFF-SegNeXt.Journal of multidisciplinary healthcare · 2024Article
- Recent advancements in machine learning for bone marrow cell morphology analysis.Frontiers in medicine · 2024Review
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5 authors.
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Abstract
Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders prompt diagnosis and patient treatment. To address this issue, we developed Morphogo, a convolutional neural network-based system for morphological examination. Morphogo was trained using a vast dataset of over 2.8 million BM nucleated cell images. Its performance was evaluated using 508 BM cases that were categorized into five groups based on the degree of morphological abnormalities, comprising a total of 385,207 BM nucleated cells. The results demonstrated Morphogo's ability to identify over 25 different types of BM nucleated cells, achieving a sensitivity of 80.95%, specificity of 99.48%, positive predictive value of 76.49%, negative predictive value of 99.44%, and an overall accuracy of 99.01%. In most groups, Morphogo cell analysis and Pathologists' proofreading showed high intragroup correlation coefficients for granulocytes, erythrocytes, lymphocytes, monocytes, and plasma cells. These findings further validate the practical applicability of the Morphogo system in clinical practice and emphasize its value in assisting pathologists in diagnosing blood disorders.
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