Key role of causal framework embedded with medical knowledge in AI-assisted diagnosis of bone marrow morphology for acute leukemia

doi:10.16150/j.1671-2870.2026.01.007

Abstract

Abstract:

Objective To construct a causal artificial intelligence (AI) framework model embedded with medical knowledge for AI-assisted diagnosis of bone marrow morphology in acute leukemia based on medical diagnostic logic. Methods A stage-by-stage causal AI diagnostic framework guided by medical knowledge was constructed using a dataset of 20 089 microscopic images of bone marrow and peripheral blood smears of 93 cases(69 patients with other diseases exhibiting normal bone marrow and peripheral blood; 24 patients with hematological diseases, including 14 cases of acute leukemia ) from Ruijin Hospital (2020-2023). Multiple specialized modules including bone marrow hyperplasia assessment module(M1), bone marrow cell classification module(M2), AKP score calculation module(M3), POX-stained leukaemic cell feature extraction module(M4), and CE -stained leukaemic cell feature extraction module(M5), were trained and tested in a 7∶3 ratio to extract key morphological features, including bone marrow hyperplasia status, the constituent ratio of bone marrow cells, and cytochemical staining patterns in both bone marrow and peripheral blood. Based on morphological characteristics of the peripheral blood and bone marrow of hematological diseases, the aforementioned specific AI recognition models were combined step by step according to medical diagnostic logic, integrating multi-dimensional morphological characteristics of bone marrow cells, and a two-stage process sequential diagnosis and prediction was carried(stage 1: integrating M1, M2, and M3;stage2: integrating modules 4 and 5). The first stage involved distinguishing between normal and abnormal bone marrow cases, while simultaneously identifying chronic myeloid leukemia. The second stage focused on the diagnosis of acute leukemia and the differentiation of its subtypes. An end-to-end AI diagnostic framework model was also established as a control and compared with the medical knowledge-embedded causal AI diagnostic framework model in real-case diagnostic testing. Results The medical knowledge-embedded causal AI diagnostic framework model achieved higher accuracy than the end-to-end AI framework model in real-case diagnostic testing at both stages (Stage 1 accuracy: 90.00% vs. 70.00%; Stage 2 accuracy: 83.33% vs. 66.67%). Conclusions Medical expertise and diagnostic logic play a crucial role in AI-assisted diagnosis of bone marrow cell morphology for hematological diseases. The application of the medical knowledge-embedded causal AI diagnostic framework model improves the accuracy and interpretability of AI-assisted diagnosis, providing a novel and generalizable paradigm for intelligent diagnosis of hematological diseases.

Key words: Bone marrow examination, Acute leukemia, Morphological diagnosis, Deep learning, Causal AI learning framework

CLC Number:

LI Miaohui, GU Junhao, HUANG Ting, QIAO Yu, CHEN Bing. Key role of causal framework embedded with medical knowledge in AI-assisted diagnosis of bone marrow morphology for acute leukemia[J]. Journal of Diagnostics Concepts & Practice, 2026, 25(01): 44-52.

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References 27

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Modules	Training set（images）	Testing set（images）	Total （images）
Bone Marrow Hyperplasia Assessment Module	140	84^a	224
Bone Marrow Cell Classification Module	9 881	4 234	14 115
AKP Score Calculation Module	1 409	1 251^b	2 660
POX-stained Leukaemic Cell Feature Extraction Module	1 480	634	2 114
CE-stained Leukaemic Cell Feature Extraction Module	683	293	976

Modules	Recall	Precision
Bone marrow cell classification module	84.36%	88.52%
POX-stained leukaemic cell feature extraction module	86.48%	90.89%
CE-stained leukaemic cell feature extraction module	85.48%	84.86%

Items	Image counts of Wright’s- stainedbone marrow (40×Magnification)	Image counts of Wright’ s-stained bone marrow (100× Magnification)	Image counts of AKP- stained peripheral blood	Image counts of POX-stained bone marrow	Image counts of CE-stained bone marrow
Normal (n=2)	14	250	210	/	/
CML-CP (n=2）	14	209	206	/	/
ALL (n=2)	14	220	154	136	135
APL (n=2）	14	190	188	108	110
AML (n=2）	14	260	46	128	130
Total	70	1129	804	372	375

Diagnostic framework	Training accuracy	Testing accuracy
Causal diagnostic framework	97.16%	90.00%
End-to-End diagnostic framework	93.48%	70.00%

Diagnostic framework	Training accuracy	Testing accuracy
Causal diagnostic framework	95.20%	83.33%
End-to-End diagnostic framework	100.00%	66.67%