ArticleJournal of medical systems2020
Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study.
Article in Journal of medical systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Artificial intelligence in cytopathological applications for cancer: a review of accuracy and analytic validity.European journal of medical research · 2024Pooled it
- Hematopathology Practice in the Digital Era: What has Changed?International journal of laboratory hematology · 2026Review
- Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability.Frontiers in medicine · 2026Article
- The Application of Artificial Intelligence-Based Bone Marrow Cell Analysis System in Pediatric Hematological Diseases.International journal of laboratory hematology · 2025Article
- A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.Scientific data · 2025Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images.Scientific reports · 2025Article
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluation of the practical application of the category-imbalanced myeloid cell classification model.PloS one · 2025Article
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.Scientific reports · 2023Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.Scientific reports · 2022Article
- Article
- The Application of Morphogo in the Detection of Megakaryocytes from Bone Marrow Digital Images with Convolutional Neural Networks.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
18 authors at 4 institutions in 1 country.
Funding
Abstract
Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8-90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.
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Registered trials
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