To evaluate the screening efficacy of AI for bone marrow cell morphology.

Bone marrow specimens of patients attending the Second Hospital of Hebei Medical University from December 1,2019 to December 21,2020;(1) Selected from one hundred bone marrow specimens, The cases included chronic myeloid cell leukemia (n=23), myelodysplastic syndrome (n=4), chronic lymphocytic leukemia (n=4), multiple myeloma (n=5), 7 acute leukemia (n=7), chronic anemia (n=32), infection (n=6) and healthy control (n=15). Including 45 males and 55 females, with age 52(37,66)years old.The bone marrow smear prepared with Wright-Giemsa, The AI analysis system and manual audit were applied to classify 13 types of bone marrow nucleated cell, taking the results of manual audit as the gold standard, comparing the difference between the results of the two methods, using statistical software to draw the confusion matrix, The compliance between the manual audit results and the pre-classification results of the AI analysis system was calculated by the Kappa consistency test method; The consistency analysis between the pre-classification results of AI and those of the manual microscopic examination was performed by the Pearson test; (2)Statistics analyzed the blast cell differential count differences of AI and manual microscopy, to evaluate the clinical application value of AI analysis system, which soured from thirty bone marrow samples of patients diagnosed with MDS and AML.

76 630 images of 13 nucleated cells were obtained by AI analysis system; the weighted average experimental diagnostic efficiency parameters of 13 types of bone marrow nucleated cells, are as follows: sensitivity(%)=95.82, specificity(%)=99.19, accuracy(%)=98.89, false positive rate(%)=0.81, false negative rate (%)=4.18; the correlation results, between the pre-classification results of AI and manual microscopic classification results,showed that blast cell, promyelocytes, neutrophilic myelocyte, neutrophilic metamyelocyte, band neutrophil, segmented neutrophi,eosinophil, basophil, polychromatic erythroblast, orthochromatic erythroblast, and lymphocytes have good positive correlation (r>0.70,all P<0.001), while basophilic erythroblast and monocytes have no obvious correlation (r=0.32,0.30, all P> 0.001); the count results of the blast cells in bone marrow smears of MDS and AML, got by AI and manual microscopy respectively, showed that the average percentage of blast cells was 8.19% by AI and 8.68% by manual microscopy in MDS, there was no significant difference between the two methods (P>0.05); the average percentage of blast cells was 48.52% by AI analysis system and 53.77% by manual microscopy in AML, and although there was a significant difference in blast cell count (P<0.01), coincidence the classification diagnostic criteria for AML (blast cells ≥ 20%).

The AI analysis system performed good sensitivity, specificity and accuracy for 13 types of bone marrow nucleated cells, which showed potential application value for the rapid classification and diagnosis of MDS and AML.

Myelodysplastic syndrome; AML; Bone marrow nucleated cells; Morphology; Artificial intelligence; Manual microscopy