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.

Artificial intelligence-assisted blood cell morphology examination of blood cells is very promising in clinical applications. Because it can significantly improve work efficiency, reduce the burden of manpower, avoid subjectivism, and facilitate standardization. The main difficulties lie in several key technical links, such as image acquisition, image segmentation, cell identification, and classification, etc. In recent years, both hardware devices and software algorithms have made rapid progress, which has led to the important development of artificial intelligence auxiliary systems from digital image acquisition, white blood cell segmentation, cell feature extraction, and classification. Compared with the traditional machine learning, the application of deep learning technology in the morphological identification of blood cells is particularly worthy of attention. In addition, the continuous emergence of microscopic blood cell image databases also provides important support for the further development and improvement of various algorithms. Understanding the key technical progress of artificial intelligence-assisted blood cell morphology examination will help to promote its continuous development and better clinical application. In recent years, artificial intelligence technology has changed from "traditional machine learning" to "deep learning", which no longer relies on manual extraction of features, but on its ability to automatically extract data to achieve. Compared with the blood cell image database from foreign countries, the construction of domestic databases should be strengthened to minimize the gap between foreign databases.