Why AI Blood Smear?

VETSCAN IMAGYST conveniently delivers AI-driven blood smear analysis, providing critical data to supplement complete blood count (CBC) results and help inform diagnosis and treatment
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RESULTS IN MINUTES

VETSCAN IMAGYST uses the accuracy of AI to efficiently read blood smears in minutes, so your staff doesn’t have to1
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SIMPLIFIED WORKFLOW

IMAGYST provides AI-driven analysis of blood smears, so your staff can focus on other tasks
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A MORE COMPLETE HEMATOLOGIC PICTURE

Blood smear data may provide important information to supplement automated CBCs for the diagnosis and/or treatment of patients who are sick or with hematologic abnormalities
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MAY IDENTIFY ABNORMALITIES SUCH AS:
  • Abnormal white blood cell count
  • Low platelet count
  • Platelet clumping
  • Changes associated with anemia

Blood smear evaluations to supplement CBC results are not often performed in veterinary clinics due to:

  • Lack of experience
  • Time
  • Training

How It Works

Prepare and scan slide in clinic using traditional methods

VETSCAN IMAGYST uses AI-driven analysis to:

CLARIFY abnormalities to supplement CBC results

PROVIDE an estimated platelet count and identify the presence of platelet clumps, which may impact platelet counts

VERIFY a WBC differential (%) and estimated count (relative count of each cell type)

  • Option to submit digital blood smear image for expert review by a clinical pathologist within the Zoetis Virtual Lab*

IDENTIFY and count polychromatophils (immature red blood cells—an indicator of a potential regenerative process) and nucleated red blood cells

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User Guide: AI Blood Smear
Download
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Integrating VETSCAN IMAGYST into a complete, in-clinic hematology solution

Use any in-clinic hematology analyzer

  • VETSCAN® HM5 is an easy-to-use option that reports a full, 5-part CBC differential with 22 parameters in <4 minutes

Get additional insights with VETSCAN IMAGYST AI Blood Smear

  • Follow up on select analyzer abnormal results
  • If abnormalities are observed, expert review via digital slide transfer is recommended*

Access expert review by a Zoetis clinical pathologist for further evaluation via digital image transfer when needed*†

Evaluate cell information not reported by AI review, including:

  • WBC—left shifts, toxic changes, malignancy
  • RBC—morphology, inclusions
  • PLT—macroplatelets

*Additional costs may apply.

†Option to send physical slide to our network of clinical pathologists as needed.

Fast, Reliable Data

VETSCAN IMAGYST delivers expert-level qualitative data to supplement CBC results

  • White blood cell differential/estimated count: neutrophils, lymphocytes, monocytes, eosinophils, basophils
  • Platelet estimated count/identifies medium and large platelet clumps
  • Identifies and counts polychromatophils as well as nucleated red blood cells

STUDY DESIGN

  • The accuracy of VETSCAN IMAGYST compared to board-certified clinical pathologists was evaluated in 59 dog and 60 cat samples
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  • Each sample was evaluated by 2 of 4 randomized clinical pathologists
  • Scatter plots were generated showing the pathologists’ results for the individual slides and sorted by percentage of cell type
  • The scatter plots show the variability between the expert pathologist reviewers (which is well documented in human and veterinary medicine literature) and was observed in all samples2-4
  • The dog lymphocyte (%) is shown below. Note the variability between pathologists
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  • The 99% prediction interval was calculated using the variation observed between expert clinical pathologists
  • The 99% prediction interval provides the estimated range expected from a future observation by an additional expert or VETSCAN IMAGYST with consideration for variability between expert clinical pathologists
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For all 5 cells of the differential (neutrophils), 93.2% to 100% of dog samples (n=59) and 88.3% to 100% of act samples (n=60) were within the 99% prediction interval of the model.

  • The VETSCAN IMAGYST canine lymphocyte classifier performance was comparable to that of an expert clinical pathologist when evaluating the monolayer of a blood smear
  • When VETSCAN IMAGYST evaluated the same slides, results were within the 99% prediction interval (calculated from expert variability) 96.6% of the time

CP=clinical pathologist.

VETSCAN IMAGYST performance accuracy was comparable to that expected of clinical pathologists1

With VETSCAN IMAGYST, expert-level WBC differential and cell counting can be performed in any clinic

IMAGYST performance1
  • WBC Differential
  • Cell Counting
Cell Type
Slides within 99% Prediction Interval
NEUTROPHIL

93.2% (55/59)

LYMPHOCYTE

96.6% (57/59)

MONOCYTE

96.6% (57/59)

EOSINOPHIL

100% (59/59)

BASOPHIL

100% (59/59)

Parameter
Slides within 95% Prediction Interval
ESTIMATED TOTAL WBC COUNT

94.9% (56/59)

ESTIMATED PLT COUNT

93.2% (55/59)

POLYCHROMATOPHIL

88.1% (52/59)
  • WBC Differential
  • Cell Counting
Cell Type
Slides within 99% Prediction Interval
NEUTROPHIL

88.3% (53/60)

LYMPHOCYTE

91.7% (55/60)

MONOCYTE

95.0% (57/60)

EOSINOPHIL

98.3% (59/60)

BASOPHIL

100% (60/60)

Parameter
Slides within 95% Prediction Interval
ESTIMATED TOTAL WBC COUNT

90.0% (54/60)

ESTIMATED PLT COUNT

83.3% (50/60)

POLYCHROMATOPHIL

95.0% (57/60)

PLT=platelet;WBC=white blood cell.

High-Resolution Images

High-resolution images from VETSCAN IMAGYST.

See what real customers say about VETSCAN IMAGYST in their practice

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References: 1. Data on file, Study No. D870R-US-21-045, 2021, Zoetis Inc. 2. Kjelgaard-Hansen M, Jensen AL. Is the inherent imprecision of manual leukocyte differential counts acceptable for quantitative purposes? Vet Clin Pathol. 2006;35(3):268-270. doi:10.1111/j.1939-165x.2006.tb00128.x. 3. Fuentes-Arderiu X, García-Panyella M, Dot-Bach D. Between-examiner reproducibility in manual differential leukocyte counting. Accred Qual Assur. 2007;12:643–645. doi:10.1007/s00769-007-0323-0. 4. Tvedten HW, Lilliehöök IE. Canine differential leukocyte counting with the CellaVision DM96Vision, Sysmex XT-2000iV, and Advia 2120 hematology analyzers and a manual method. Vet Clin Pathol. 2011;40(3):324-339. doi:10.1111/j.1939-165X.2011.00347.x.

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