ArticleScientific reports2024
Deep learning assists in acute leukemia detection and cell classification via flow cytometry using the acute leukemia orientation tube.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Review
- Recent advances in deep learning for leukemia diagnosis: a scoping review of diagnostic modalities and fusion-based approaches.Frontiers in digital health · 2026Review
- Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection.Frontiers in medicine · 2026Article
- Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP.BioData mining · 2025Article
- Neuro-Bridge-X: A Neuro-Symbolic Vision Transformer with Meta-XAI for Interpretable Leukemia Diagnosis from Peripheral Blood Smears.Diagnostics (Basel, Switzerland) · 2025Article
- Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.Annals of surgical oncology · 2025Article
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Enhancing Blood Cell Diagnosis Using Hybrid Residual and Dual Block Transformer Network.Bioengineering (Basel, Switzerland) · 2025Article
- Multiplex immunophenotyping of human acute myeloid leukemia patients revealed single -cell heterogeneity with special attention on therapy sensitive and therapy resistant subpopulations.Frontiers in immunology · 2025Article
- CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis.Frontiers in cell and developmental biology · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Acute Myeloid Leukemia in Older Patients: From New Biological Insights to Targeted Therapies.Current oncology (Toronto, Ont.) · 2024Review
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7 authors.
Funding
Abstract
This study aimed to evaluate the sensitivity of AI in screening acute leukemia and its capability to classify either physiological or pathological cells. Utilizing an acute leukemia orientation tube (ALOT), one of the protocols of Euroflow, flow cytometry efficiently identifies various forms of acute leukemia. However, the analysis of flow cytometry can be time-consuming work. This retrospective study included 241 patients who underwent flow cytometry examination using ALOT between 2017 and 2022. The collected flow cytometry data were used to train an artificial intelligence using deep learning. The trained AI demonstrated a 94.6% sensitivity in detecting acute myeloid leukemia (AML) patients and a 98.2% sensitivity for B-lymphoblastic leukemia (B-ALL) patients. The sensitivities of physiological cells were at least 80%, with variable performance for pathological cells. In conclusion, the AI, trained with ResNet-50 and EverFlow, shows promising results in identifying patients with AML and B-ALL, as well as classifying physiological cells.
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