Abstract
Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.
Highlights
The incidence of hematopoietic and lymphoid malignancies is increasing worldwide. [1] Bone marrow (BM) aspirate examination is a critical step in the initial work-up for hematological diseases
Cell classification utilizing artificial intelligence (AI) algorithms embedded in the system was preliminarily validated with 145 BM smears collected from Sir Run Run Shaw Hospital (SRRSH)
These results demonstrated that the algorithms performed well in cell classification for BM smears at SRRSH
Summary
The incidence of hematopoietic and lymphoid malignancies is increasing worldwide. [1] Bone marrow (BM) aspirate examination is a critical step in the initial work-up for hematological diseases. For the microscopic examination of peripheral blood smear, several instruments have been developed and proved to be efficient in digital morphological analysis, such as DM9600, DI-60, Cobas M511 and Vision Hema. [5,6,7,8] These systems have made promising advancements in automation, digitization, standardization and intellectualization of peripheral blood smear analysis. [9,10,11,12,13] In addition, more technical challenges are present in the marrow specimen including the use of oil-immersion lens for slide digitization, scanning field selection, and development of proper focusing algorithms. [20, 21] There are systems currently under investigation for analyzing BM smears, such as Vision Bone Marrow [22] and Scorpio Full Field BMA. Recommendation. [25] Smears completely diluted by peripheral blood, with unclear patient information, or considered to be inadequate by investigators were excluded
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