Establishment and validation of intelligent detection model for acute promyelocytic leukemia based on contrastive learning in complete blood cell analysis
Objective To establish an intelligent detection algorithm model for acute promyelocytic leukemia(M3 model)based on a contrast large model using machine learning statistical software and validate its effectiveness.Methods The data from 8 256 outpa-tients and inpatients who underwent complete blood cell analysis at Peking Union Medical College Hospital were retrieved and analyzed using the laboratory information system(LIS)and hospital information system(HIS).A M3 screening model was established and vali-dated using the data from outpatients and inpatients who underwent complete blood cell analysis at our hospital from July to October 2023.Results The M3 model demonstrated potential application value in screening for M3 disease in complete blood cell analysis,which showed certain efficacy in screening for neutrophil toxicity changes,particularly in identifying two cases of blue-green inclusion bodies in neutrophils.Conclusion The M3 model exhibited low specificity for M3 diagnosis.Future research should focus on increas-ing the number of M3-positive cases to optimize the model,ensuring high sensitivity while improving specificity.This model will provide assistance for the intelligent review of complete blood cell analysis.
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