Canine Cerebrospinal Fluid Analysis Using Two New Automated Techniques: The Sysmex XN-V Body Fluid Mode and an Artificial-Intelligence-Based Algorithm

Simple Summary Cerebrospinal fluid analysis is an important diagnostic test for a neurological canine patient. For this analysis, the total nucleated cell count and differential cell counts are routinely taken, but both involve time-consuming manual methods. The Sysmex XN-V body fluid mode is a specific setting used for analyzing different body fluids, including cerebrospinal fluid samples, while the deep-learning-based algorithm generated by the Olympus VS200 slide scanner allows recognition and classification of various cell types on scanned slides. In this study, the faster automated methods mentioned above were assessed by comparing them with the manual methods for both the total nucleated and differential cell counts. Manually set gates were used for the Sysmex body fluid mode following incorrect cell classifications recorded when using the predefined settings. This study demonstrates that automated methods may be used for total nucleated cell count assessments in canine cerebrospinal fluid samples, but optimization is still needed for both differential counts.Abstract Cerebrospinal fluid analysis is an important diagnostic test when assessing a neurological canine patient. For this analysis, the total nucleated cell count and differential cell counts are routinely taken, but both involve time-consuming manual methods. To investigate faster automated methods, in this study, the Sysmex XN-V body fluid mode and the deep-learning-based algorithm generated by the Olympus VS200 slide scanner were compared with the manual methods in 161 canine cerebrospinal fluid samples for the total nucleated cell count and in 65 samples with pleocytosis for the differential counts. Following incorrect gating by the Sysmex body fluid mode, all samples were reanalyzed with manually set gates. The Sysmex body fluid mode then showed a mean bias of 15.19 cells/mu L for the total nucleated cell count and mean biases of 4.95% and -4.95% for the two-part differential cell count, while the deep-learning-based algorithm showed mean biases of -7.25%, -0.03% and 7.27% for the lymphocytes, neutrophils and monocytoid cells, respectively. Based on our findings, we propose that the automated Sysmex body fluid mode be used to measure the total nucleated cell count in canine cerebrospinal fluid samples after making adjustments to the predefined settings from the manufacturer. However, the two-part differential count of the Sysmex body fluid mode and the deep-learning-based algorithm require some optimization.