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SediVue Dx images
The SediVue Dx Urine Sediment Analyzer takes 70 high-resolution digital images for every patient
run. These images are equivalent to 45 high-power fields of view using a 40x objective. Using a
proprietary neural network algorithm, each of the 70 images is scanned to identify, classify, and
count the formed elements. Each image is given a score based on the absence or presence of
formed elements and the clinical significance for each found. The images are then sorted, with
the highest scoring images displayed first and lowest scoring images displayed last. The top three
images are automatically retained by the SediVue Dx and transmitted to VetConnect* PLUS.
Image review
Similar to manual microscopy, the sample images should be reviewed with every sample run in
order to arrive at consensus of the results. This process can take as little as a few seconds. The
value in reviewing the images is to validate the numerical data provided and potentially supplement
the SediVue Dx analysis, if needed.
Image tags
Image tags are abbreviated labels for the reported parameters that appear on the formed elements
found in the sample. Image tags may not be available:
• If the image is out of focus or flagged for review.
• If the sample is from an unvalidated species or fluid.
• When the results are None to Rare or Suspect Presence (for casts).
• For bacteria results.
Image tags can be toggled on and off.
Convolutional neural network
SediVue Dx’s convolutional neural network is a proprietary pattern-recognition algorithm that works
similar to facial recognition software. The Neural Network leverages a veterinary-specific image
library that is used to train and grow the algorithm over time. It is what drives the identification,
classification, and count of each of the formed elements.
Bacteria; rods with white and red
blood cells
Bacteria; cocci with white blood
cells
Large struvite crystals
Ammonium biurate (thorn apple)
crystals
Hyaline cast
Bilirubin crystal with
RBCs and WBCs
Getting Started
Содержание SediVue Dx
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