ArticleTechnology in cancer research & treatment
The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.European journal of medical research · 2024Pooled it
- Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.African journal of laboratory medicine · 2026Review
- 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
- The Role of Megakaryocyte Assessment in Bone Marrow Cytology.Journal of clinical medicine · 2025Review
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Recent advancements in machine learning for bone marrow cell morphology analysis.Frontiers in medicine · 2024Review
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Novel Applications of Artificial Intelligence in Cancer Research.Technology in cancer research & treatmentArticle
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evaluation of megakaryocytes is an important part of the work up on bone marrow smear examination. It has significance in the differential diagnosis, therapeutic efficacy assessment, and predication of prognosis of many hematologic diseases. The process of manual identification of megakaryocytes are tedious and lack of reproducibility; therefore, a reliable method of automated megakaryocytic identification is urgently needed. Three hundred and thirty-three bone marrow aspirate smears were digitized by Morphogo system. Pathologists annotated megakaryocytes on the digital images of marrow smears are applied to construct a large dataset for testing the system's predictive performance. Subsequently, we obtained megakaryocyte count and classification for each sample by different methods (system-automated analysis, system-assisted analysis, and microscopic examination) to study the correlation between different counting and classification methods. Morphogo system localized cells likely to be megakaryocytes on digital smears, which were later annotated by pathologists and the system, respectively. The system showed outstanding performance in identifying megakaryocytes in bone marrow smears with high sensitivity (96.57%) and specificity (89.71%). The overall correlation between the different methods was confirmed the high consistency (
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