howitworks

Elevate your practice's diagnostic offerings with the latest in fecal flotation technology

Performance

VETSCAN IMAGYSTTM is powered by deep-learning to help veterinarians make accurate and timely intestinal parasitic diagnoses for patients1,2

The IMAGYST algorithm and fecal sample preparation techniques were vigorously tested1,2

In two studies, the algorithm performance of IMAGYST closely matched that of an expert board-certified parasitologist, with the diagnostic sensitivity and specificity of the comparison ranging between 75.8%-100% and 80.4%-100%, respectively, across six genera/family of parasites: Ancylostoma, Toxocara, Trichuris, Cystoisospora and Giardia.1,2

Algorithm chart

The egg, cyst and oocyst recovery performance of the IMAGYST preparation device with centrifugation was compared to recovery performance of a conventional centrifugal flotation reference method. All samples were read by an expert board-certified parasitologist.1,2

In the first study, agreement (defined as the number of true positives and true negatives divided by the total number of samples) between the two methods was 94.5%.1 In the second study, agreement between the two methods was 94.3%.2

Recovery chart

Image recognition

IMAGYST has the ability to learn over time1

Images captured by IMAGYST from practices and institutions are stored in the cloud

Images in the cloud
  • The cloud creates the ability to easily share images as teaching and training materials for new staff and technicians
  • IMAGYST utilizes VETSCAN FUSE, a bi-directional communication system between Practice Information Management Software (PIMS) and IMAGYST, allowing easy access to patient results, updates to medical records and charge capturing

    • IMAGYST is compatible with select PIMS systems. Contact your Zoetis representative for the full list
PIMS example image

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References: 1. Nagamori Y, Sedlak RH, DeRosa A, et al. Evaluation of the VETSCAN IMAGYST: an in‑clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm. Parasites Vectors. 2020;13:346. doi:10.1186/s13071-020-04215-x. 2. Data on File, Study Report No. D870R-US-19-008, 2020, Zoetis Inc.

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