Application progress of artificial intelligence in morphological diagnosis of blood diseases

doi:10.16150/j.1671-2870.2026.01.003

Abstract

Abstract:

With the widespread adoption of whole-slide scanning and the advancement of digital pathology techno-logy, the use of artificial intelligence (AI) for the analysis of peripheral blood and bone marrow smears has achieved notable breakthroughs, including the application of object detection (You Only Look Once, YOLO), weakly supervised contrastive learning, and multiple instance learning (MIL) in addressing cell recognition and domain shifts. Among them, MIL has been successfully applied in the auxiliary diagnosis of acute promyelocytic leukemia (M₃), effectively providing early warning of critical cases through global feature aggregation of the entire smear. Studies indicate that the accuracy of AI in determining subtypes of myeloproliferative neoplasm (MPN) is as high as 93.1%. The area under the receiver operating characteristic curve for AI predicting the risk of transformation to AML in patients with myelodysplastic syndrome (MDS) based on morphological features is 0.81. The correlation coefficient between AI-based automated quantification of hematopoietic tissue and pathologist assessment results reaches 0.78, confirming the value of AI in bone marrow biopsy pathological analysis, especially highlighting the potential of whole-slide-level models for clinical application. In terms of clinical practice application, a standardized model of AI pre-classification combined with manual review has been established for peripheral blood smear analysis. In bone marrow biopsy pathological analysis, although AI has achieved digital scanning, its current applications remain limited to cell counting and preliminary screening due to the complexity of cell lineages and the heterogeneity of dysplastic hematopoiesis. Furthermore, digital remote consultation plays an important role in alleviating the uneven distribution of medical resources. In the future, with the maturation of multi-modal fusion and large language model (LLM)-based report generation technology, AI is expected to evolve from a simple counting and classification tool into a comprehensive model integrating diagnosis, subtyping, and prognosis assessment.

Key words: Artificial intelligence, Hematopathy, Morphology, Peripheral blood smear, Bone marrow biopsy

CLC Number:

R445
R733.7

WANG Yan, FAN Lei. Application progress of artificial intelligence in morphological diagnosis of blood diseases[J]. Journal of Diagnostics Concepts & Practice, 2026, 25(01): 15-20.

References 19

[1]	LU M Y, WILLIAMSON D F K, CHEN T Y, et al. Data-efficient and weakly supervised computational pathology on whole-slide images[J]. Nat Biomed Eng, 2021, 5(6):555-570. doi: 10.1038/s41551-020-00682-w pmid: 33649564
[2]	ECKARDT J N, MIDDEKE J M, RIECHERT S, et al. Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears[J]. Leukemia, 2022, 36(1):111-118. doi: 10.1038/s41375-021-01408-w
[3]	CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[J/OL]. [2025-12-09]. http://arxiv.org/pdf/2002.05709.
[4]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,2016:779-788.
[5]	TAYEBI R M, MU Y, DEHKHARGHANIAN T, et al. Automated bone marrow cytology using deep learning to generate a histogram of cell types[J]. Commun Med, 2022,2:45.
[6]	MATEK C, KRAPPE S, MÜNZENMAYER C, et al. Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set[J]. Blood, 2021, 138(20):1917-1927. doi: 10.1182/blood.2020010568 pmid: 34792573
[7]	XIE S, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE,2017: 5987-5995.
[8]	WANG Y, LIU H, WANG H, et al. Enhancing morphological analysis of peripheral blood cells in chronic lymphocytic leukemia with an artificial intelligence-based tool[J]. Leuk Res, 2023,130:107310.
[9]	OSMAN M, AKKUS Z, JEVREMOVIC D, et al. Classification of monocytes, promonocytes and monoblasts using deep neural network models: An area of unmet need in diagnostic hematopathology[J]. J Clin Med, 2021, 10(11):2264. doi: 10.3390/jcm10112264 URL
[10]	CAICEDO J C, MCQUIN C, GOODMAN A, et al. Weakly supervised learning of single-cell feature embeddings[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE,2018: 9309-9318.
[11]	MANESCU P, NARAYANAN P, BENDKOWSKI C, et al. Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning[J]. Sci Rep, 2023,13:2562.
[12]	MA J, GUO Z, ZHOU F, et al. A generalizable pathology foundation model using a unified knowledge distillation pretraining framework[J]. Nat Biomed Eng, 2025.
[13]	WANG R, SHI Z, ZHANG Y, et al. Development and validation of deep learning model for diagnosis and subtypes differentiation of myeloproliferative neoplasms using clinical data and digital pathology[J]. Blood, 2023, 142(Supplement 1):123. doi: 10.1182/blood-2023-184459 URL
[14]	BRÜCK O E, LALLUKKA-BRÜCK S E, HOHTARI H R, et al. Machine learning of bone marrow histopathology identifies genetic and clinical determinants in patients with MDS[J]. Blood Cancer Discov, 2021, 2(3):238-249. doi: 10.1158/2643-3230.BCD-20-0162 URL
[15]	VAN EEKELEN L, PINCKAERS H, VAN DEN BRAND M, et al. Using deep learning for quantification of cellula-rity and cell lineages in bone marrow biopsies and comparison to normal age-related variation[J]. Pathology, 2022, 54(3):318-327. doi: 10.1016/j.pathol.2021.07.011 URL
[16]	LEE L H, MANSOOR A, WOOD B, et al. Performance of CellaVision DM96 in leukocyte classification[J]. J Pathol Inform, 2013, 4(1):14.
[17]	REZAEIKHONAKDAR D. AI chatbots and challenges of HIPAA compliance for AI developers and vendors[J]. J Law Med Ethics, 2023, 51(4):988-995. doi: 10.1017/jme.2024.15 URL
[18]	MINSSEN T, SOLAIMAN B, KÖTTERING L, et al. Gover-ning AI in the European Union: emerging infrastructures and regulatory ecosystems in health[M]//SOLAIMAN B, COHEN I G. Research handbook on health, AI and the Law Cheltenham. UK: Edward Elgar Publishing Ltd, 2024,311-331.
[19]	WANG X, ZHAO J, MAROSTICA E, et al. A pathology foundation model for cancer diagnosis and prognosis prediction[J]. Nature, 2024, 634(8035):970-978. doi: 10.1038/s41586-024-07894-z