Automated subtype classification of acute leukemia from bone marrow smears using a cross-platform deep learning model Free

Introduction: Accurate morphological classification of acute leukemia (AL) subtypes from bone marrow smears (BMSs) remains a cornerstone of the MICM (morphology–immunophenotyping–cytogenetics–molecular biology) diagnostic framework and is critical for guiding clinical decisions and prognostic assessment. However, manual evaluation of leukemic cell morphology is labor-intensive, time-consuming, and highly dependent on expert hematopathological interpretation. Although recent artificial intelligence (AI)-based methods have shown promise in automating cell recognition, existing approaches are often limited by their narrow coverage of leukemic subtypes and insufficient generalizability across different imaging platforms. To overcome these limitations, we developed a large diagnosis-oriented single cell image dataset and trained ALSNet (acute leukemia subtyping network), a cross-platform deep learning model designed for automated and robust subtyping of AL using region-of-interest images derived from BMSs.

Methods: A total of 1,232 Wright–Giemsa–stained bone marrow smears (BMSs) from patients at initial diagnosis were collected and digitized under 100× oil immersion using both manual microscopes and two automated scanning platforms. The dataset included 210 normal controls, 210 acute lymphoblastic leukemia (ALL), 150 acute promyelocytic leukemia (APL), and 662 acute myeloid leukemia (AML) cases. Cell annotation was performed using a human-in-the-loop framework, integrating model-assisted pre-classification to improve efficiency and consistency. A Mask R-CNN–based detection and segmentation network was first employed to extract single-cell images from raw multi-cell fields. A CNN based on the GoogLeNet architecture was trained to exclude low-quality images containing artifacts, ruptured cells, or debris. To ensure generalizability across platforms, CIELAB-based histogram matching was used for color normalization. After preprocessing, single-cell images were categorized into 19 cytomorphologic classes relevant to leukemia diagnosis. These images were used to train ALSNet, a ResNeXt-based deep learning model with a dual-branch Transformer module, to perform cell-level classification and leukemia subtyping according to the WHO classification. Subtyping encompassed ALL, APL, and diverse AML subtypes, including AML without maturation, AML with maturation, acute myelomonocytic leukemia (AMML), acute monoblastic leukemia (AMBL), acute monocytic leukemia (AMOL), acute erythroid leukemia (AEL), and acute megakaryoblastic leukemia (AMKL). Model performance was validated on an independent testing cohort consisting of 100 AL cases and 10 normal controls imaged using a separate scanning platform.

Results: Analysis of clinical characteristics revealed different baseline features among leukemic subtypes, such as more significant thrombocytopenia in APL and AMKL. Segmentation and quality control (QC) modules showed high accuracy (AUC = 0.9772 for segmentation; AUC = 0.9926 for QC filtering). Following expert-verified annotation, a total of 182,230 high-quality single-cell images were compiled to establish a large-scale cytomorphologic training dataset. The ALSNet model achieved a macro-averaged accuracy of 0.912 in cell-level classification across 19 categories. At the case level, the model correctly identified normal samples, ALL, APL and AML subtypes with an overall accuracy of 0.75 in the testing set, demonstrating cross-platform robustness.Conclusions: This study presents ALSNet, a cross-platform AI model capable of automated, high-accuracy subtype classification of AL from routine BMS images. By integrating robust image preprocessing, single-cell recognition, and advanced classification architecture, ALSNet addresses key limitations in previous AI diagnostic efforts. The model's generalizability across imaging systems and its alignment with clinical phenotypes support its potential for real-world deployment in hematologic diagnostics.