The avian hemogram is usually performed in veterinary diagnostic laboratories by using manual cell counting techniques and differential counts determined by light microscopy. There is no standard automated technique for avian blood cell count and differentiation to date. These shortcomings in birds are primarily because erythrocytes and thrombocytes are nucleated, which precludes the use of automated analyzers programmed to perform mammal complete blood cell counts. In addition, there is no standard avian antibody panel, which would allow cell differentiation by immunophenotyping across all commonly seen bird species. We report an alternative hematologic approach for quantification and differentiation of avian blood cells by using high-throughput image cytometry on blood smears in psittacine bird species. A pilot study was designed with 70 blood smears of different psittacine bird species stained with a Wright-Giemsa stain. The slides were scanned at 0.23 μm/pixel. The open-source softwares CellProfiler and CellProfiler Analyst were used for analyzing and sorting each cell by image cytometry. A “pipeline” was constructed in the CellProfiler by using different modules to identify and export hundreds of measures per cell for shape, intensity, and texture. Rules for classifying the different blood cell phenotypes were then determined based on these measurements by iterative feedback and machine learning by using CellProfiler Analyst. Although this approach shows promises, avian Leukopet results could not be duplicated when using this technique as is. Further studies and more standardized prospective investigations may be needed to refine the “pipeline” strategy and the machine learning algorithm.
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