ArticleScientific reports2023
Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning.
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, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.International journal of laboratory hematology · 2026Review
- Article
- IL-6 Upregulates CD73 in Human Placental Mesenchymal Stem Cells to Promote ATRA-Induced NB4 Cell Differentiation in Acute Promyelocytic Leukemia.Cancer medicine · 2026Article
- Illusion of competence: vision-language models provide confident but inaccurate explanations in cytological diagnostics.Scientific reports · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- AI-based hematological malignancy prediction from peripheral blood smears in a large diagnostic laboratory cohort.Leukemia · 2026Article
- Interpretable multiple instance learning for hematologic diagnosis from peripheral blood smears.Communications medicine · 2026Article
- GPTNeXt: Biomedical Image Classification Investigations.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal AI reading of genetic complexity and residual marrow composition in acute promyelocytic leukemia.Frontiers in immunology · 2026Article
- Recent advances in deep learning for leukemia diagnosis: a scoping review of diagnostic modalities and fusion-based approaches.Frontiers in digital health · 2026Review
- Tissue-Resident Myeloid and Histiocytic Cells in Health and Disease: Novel Emerging Concepts.American journal of hematology · 2025Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- Interpretable Multiple Instance Learning for Hematologic Diagnosis from Peripheral Blood Smears.Research square · 2025Article
- Applications of Artificial Intelligence in Acute Promyelocytic Leukemia: An Avenue of Opportunities? A Systematic Review.Journal of clinical medicine · 2025Review
- Artificial intelligence-assisted early screening of acute promyelocytic leukaemia in blood smears: a prospective evaluation of MC-100i.Frontiers in oncology · 2025Article
- Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.HemaSphere · 2024Review
- Advancements in leukemia management: Bridging diagnosis, prognosis and nanotechnology (Review).International journal of oncology · 2024Review
- Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.Journal of pathology informatics · 2024Article
- Article
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.