Search

Introduction: A peripheral blood smear is a basic test for hematological disease diagnosis. This test is performed manually in many places worldwide, which requires both time and qualified staff. Large laboratories are equipped with digital morphology analyzers, some of which are based on deep learning methods. However, it is difficult to explain to scientists how they work. In this paper, we proposed to add an explanatory factor to enhance the interpretability of deep learning models in leukocyte classification. Methods: 10 297 single images of leukocytes obtained from peripheral blood smears were included in this study. Pre‐trained and fully trained VGG16 and VGG19 models were used to classify the leukocytes, and Shapley Additive Explanations (SHAP) DeepExplainer was applied to visualize the area of cells that were significant for classification. The output images from the DeepExplainer were compared with cellular elements that are essential to laboratory practice. Results: The accuracy of our fully trained models was 99.81% for VGG16 and 99.79% for VGG19. It achieved slightly better results than the partially trained model, which scored 98.67% for VGG16 and 98.33% for VGG19. Their SHAP explanations indicated the significance of cellular structures in microscopic examination. Explanations in the pre‐trained models have proved the cell and nucleus contours to be relevant to classification, while explanations in the fully trained models pointed to the cytoplasm area. Conclusion: Despite different SHAP DeepExplainer explanations for fully and partially trained models, this method appears to be helpful for the verification of leukocyte classification in automated peripheral blood smear examination. [ABSTRACT FROM AUTHOR]

Introduction: Management of hemophilia A has changed significantly in the past few years with the expansion of new and/or modified products as treatment options. Unfortunately, many of the standard factor VIII assays do not always accurately measure all available treatment products; therefore, the laboratory must investigate various assay algorithms to ensure the reporting of the correct results. Methods: Requirements for factor testing, diagnosis and severity levels, product testing, factor VIII inhibitor detection and titers, are evaluated, and potential algorithms are created for optimal assessment of patients with hemophilia A. Results: The potential for inaccurate result reporting for patients with hemophilia A or those being treated with the myriad of products has left many laboratories uncertain as to which assay algorithm to implement to ensure reporting the correct results for all products used in their hemophilia program. Algorithms for using either One‐stage Clotting assays or Chromogenic assays or a combination of both types of assays are presented for each laboratory to implement based on their clinical situation. Conclusions: Several algorithms are considered based on the needs of the clinical providers and their patients. Each laboratory must select a testing algorithm that is cost‐effective and within available resources, yet that encompasses the needs of their providers and patients. Laboratory personnel must consider all assay uses (factor VIII levels, different products, interfering products, and inhibitor titers) in determining the best algorithm for their laboratory. This paper is a starting guide for developing the best factor VIII testing assays and protocols for your laboratory. [ABSTRACT FROM AUTHOR]