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Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.

John Donald Marra^1,2, Gina Zini¹

¹Sezione di Ematologia, Dipartimento di Scienze Ematologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy.

International Journal of Laboratory Hematology

|June 20, 2026

Summary

Artificial intelligence (AI) shows promise for automating blood and bone marrow analysis in leukemia diagnosis. While AI improves efficiency, current systems need further validation for accurate blast detection before routine clinical use.

Keywords:

acute leukemia artificial intelligence blast detection bone marrow aspirate convolutional neural networks digital morphology hematology laboratory leukocyte classification machine learning peripheral blood smear

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Area of Science:

Hematology
Medical Diagnostics
Artificial Intelligence

Background:

Morphologic evaluation of peripheral blood (PB) smears and bone marrow aspirates (BMA) is crucial for acute leukemia diagnosis, but it is time-consuming and prone to variability.
Artificial intelligence (AI), especially deep learning, offers potential for automated leukocyte classification and blast detection, addressing limitations of manual methods.

Purpose of the Study:

To conduct a narrative review of AI-based approaches for morphologic evaluation in hematology, specifically for blast recognition and leukemia screening.
To analyze classical machine learning and deep learning methodologies applied to PB smears and BMA samples.
To review commercial digital morphology platforms for their blast detection performance.

Main Methods:

A narrative literature review was performed.
Analysis included classical machine learning and deep learning techniques applied to peripheral blood and bone marrow samples.
Commercial digital morphology platforms were assessed for blast detection capabilities.

Main Results:

Deep learning models, particularly convolutional neural networks, demonstrate near-human accuracy (>90% sensitivity/specificity) for blast detection in PB smears.
Classical machine learning shows moderate performance in blast recognition, limited by manual feature selection.
AI performance in BMA analysis is more variable due to sample complexity; commercial platforms excel in mature cell classification but show modest accuracy for immature/neoplastic cells.

Conclusions:

AI-based digital morphology systems are promising for hematology labs, enhancing efficiency and standardization.
Current limitations in blast detection accuracy and generalizability prevent standalone diagnostic use; further validation and interpretability are needed.
Widespread adoption requires rigorous validation, regulatory approval, and quality assurance, with caution advised for AI-driven BMA systems per ICSH guidelines.