ReviewHemaSphere2024
Models for the marrow: A comprehensive review of AI-based cell classification methods and malignancy detection in bone marrow aspirate smears.
Review in HemaSphere, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.International journal of laboratory hematology · 2026Review
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Digital Pathology in Hematopathology: From Vision to Deployment.International journal of laboratory hematology · 2026Review
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Biomarkers and advances in AML-MRC: from bench to bedside.Annals of hematology · 2026Review
- Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples.Scientific reports · 2026Article
- Review
- Advancements in bone marrow biopsy: the role of omics and artificial intelligence in hematologic diagnostics.Frontiers in medicine · 2026Review
- Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.Medical oncology (Northwood, London, England) · 2025Review
- ILViT: An Inception-Linear Attention-Based Lightweight Vision Transformer for Microscopic Cell Classification.Journal of imaging · 2025Article
- Real-World Application of Digital Morphology Analyzers: Practical Issues and Challenges in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2025Review
- Detection of acute myeloid leukemia and remission states using heterogeneous flow cytometry data.Frontiers in oncology · 2025Article
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the high prevalence of artificial intelligence (AI) research in medicine, the development of deep learning (DL) algorithms based on image recognition, such as the analysis of bone marrow aspirate (BMA) smears, is rapidly increasing in the field of hematology and oncology. The models are trained to identify the optimal regions of the BMA smear for differential cell count and subsequently detect and classify a number of cell types, which can ultimately be utilized for diagnostic purposes. Moreover, AI is capable of identifying genetic mutations phenotypically. This pipeline has the potential to offer an accurate and rapid preliminary analysis of the bone marrow in the clinical routine. However, the intrinsic complexity of hematological diseases presents several challenges for the automatic morphological assessment. To ensure general applicability across multiple medical centers and to deliver high accuracy on prospective clinical data, AI models would require highly heterogeneous training datasets. This review presents a systematic analysis of models for cell classification and detection of hematological malignancies published in the last 5 years (2019-2024). It provides insight into the challenges and opportunities of these DL-assisted tasks.
Identifiers
What Socratic holds
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 Socratic graph.