ArticleScientific reports2019
A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 93 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Cross-modal fusion of cytomorphology andFrontiers in immunology · 2026Article
- Artificial Intelligence in Haematologic Diagnostics: Current Applications and Future Perspectives.Acta haematologica · 2026Review
- Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- AI-based detection of neutrophil dysplasia: an accessible and sensitive model for MDS diagnosis from peripheral blood.Annals of hematology · 2025Article
- The utility of automated artificial intelligence-assisted digital cytomorphology for bone marrow analysis in diagnostic haemato-oncology.Clinical and translational medicine · 2025Article
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.BMC medical informatics and decision making · 2025Article
- Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.Cancer science · 2025Article
- Unlocking the Potential of Artificial Intelligence in Acute Myeloid Leukemia and Myelodysplastic Syndromes.Current hematologic malignancy reports · 2024Review
- Deep Learning: A Primer for Neurosurgeons.Advances in experimental medicine and biology · 2024Review
- Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects.Cancers · 2023Review
- Automated bone marrow cell classification through dual attention gates dense neural networks.Journal of cancer research and clinical oncology · 2023Article
- A machine learning tool for the diagnosis of SARS-CoV-2 infection from hemogram parameters.Journal of cellular and molecular medicine · 2023Article
- Quantitative interpretation of bone marrow biopsies in MPN-What's the point in a molecular age?British journal of haematology · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which diagnosis is mainly based on morphological findings of PB and bone marrow. We developed an automated diagnostic support system of MDS by combining an automated blood cell image-recognition system using a deep learning system (DLS) powered by convolutional neural networks (CNNs) with a decision-making system using extreme gradient boosting (XGBoost). The DLS of blood cell image-recognition has been trained using datasets consisting of 695,030 blood cell images taken from 3,261 PB smears including hematopoietic malignancies. The DLS simultaneously classified 17 blood cell types and 97 morphological features of such cells with >93.5% sensitivity and >96.0% specificity. The automated MDS diagnostic system successfully differentiated MDS from aplastic anemia (AA) with high accuracy; 96.2% of sensitivity and 100% of specificity (AUC 0.990). This is the first CNN-based automated initial diagnostic system for MDS using PB smears, which is applicable to develop new automated diagnostic systems for various hematological disorders.
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