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

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.