ABSTRACT
Objective:
To evaluate the performance of an artificial intelligent (AI)-based automated digital
cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral
white blood cells (WBCs).
Methods:
A multi-center study. 1. A total of 3010 venous
blood samples were collected from 11
tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-
classification results were compared with the post-
classification results reviewed by senior morphological experts in evaluate the accuracy,
sensitivity,
specificity, and agreement of the AI morphology analyzers on the WBC pre-
classification. 2. 400
blood samples (no less than 50% of the samples with abnormal WBCs after pre-
classification and manual
review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-
classification and the manual microscopic examination results was analyzed. 3.
Blood samples of
patients diagnosed with
lymphoma,
acute lymphoblastic leukemia,
acute myeloid leukemia,
myelodysplastic syndrome, or myeloproliferative
neoplasms were selected from the 3 010
blood samples. The performance of the AI morphology analyzers in these five
hematological malignancies was evaluated by comparing the pre-
classification and post-
classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-
classification and expert audit results, and Passing-Bablock
regression analysis was used for comparison test, and accuracy,
sensitivity,
specificity, and agreement were calculated according to the formula.
Results:
1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated
red blood cells. Compared with the post-
classification results reviewed by senior morphological experts, the pre-
classification accuracy of total WBCs reached 97.97%, of which the pre-
classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-
classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated
red blood cells (
neutrophils,
lymphocytes,
monocytes,
eosinophils,
basophils, immature
granulocytes, blast
cells,
nucleated erythrocytes and malignant
cells r>0.90 respectively, reactive
lymphocytes r=0.85). With reference, the positive smear of abnormal
cell types defined by The International
Consensus Group for
Hematology, the AI morphology analyzer has the
similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the
blood samples with malignant
hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast
cells pre-
classification, and the sensitivities were higher than 94%. In
acute myeloid leukemia, the
sensitivity of abnormal
promyelocytes pre-
classification exceeded 95%.
Conclusion:
The AI morphology analyzer showed high pre-
classification accuracies and sensitivities on all types of
leukocytes in peripheral
blood when comparing with the post-
classification results reviewed by experts. The post-
classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive
white blood cell detection method for
screening malignant
hematological diseases.