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Transforming everyday practice

Introducing Eko Vet+™ | CANINEBEAT® AI – designed to enhance murmur detection and grading*, improve owner communication and support earlier, data-based action. The three-part solution that works with your current stethoscope and clinical routine adding expert-validated AI support in every auscultation.

Developed through a collaboration between Boehringer Ingelheim, Eko Health and a network of veterinary cardiologists, Eko Vet+™ | CANINEBEAT® AI provides an end-to-end solution for confident murmur detection and improved communication with owners.

Watch our video to learn more about Eko Vet+ | CANINEBEAT® AI

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"This will be a game changer in the world of cardiology" Professor Gerhard Wess Dipl. ACVIM/ECVIM-CA  (Cardiology), Dipl. ECVIM-CA (Internal Medicine)

CANINEBEAT® AI algorithm: Validated heart murmur detection

Boehringer Ingelheim and veterinary cardiologists worldwide have combined decades of veterinary canine cardiology expertise with extensive experience in AI-powered digital auscultation from Eko Health to deliver CANINEBEAT® AI, an algorithm that is robust, clinically relevant and validated against real-world scenarios.

This collaboration aims to make heart murmur detection more accurate, accessible and easier to communicate in everyday veterinary practice.

A standing Cavalier King Charles Spaniel

Canine heart disease

A sitting Daschund

Education and resources

Discover educational resources covering the challenges of auscultation in canine cardiology, to the rise of artificial intelligence in veterinary medicine.

discover resources

 

*CANINEBEAT® AI analyses heart sounds and classifies murmur loudness using a modified Levine intensity scale.

Reference: 1. Keene BW, Atkins CE, Bonagura JD, et al. ACVIM consensus guidelines for the diagnosis and treatment of myxomatous mitral valve disease in dogs. J Vet Intern Med. 2019;33:1127–1140. 2. Christer Ahlström (2006). Processing of the Phonocardiographic Signal − Methods for the Intelligent Stethoscope. Doctoral dissertation. Department of Biomedical Engineering, Linköpings universitet, Sweden. 3. The large, epidemiological LISTEN Study. 2026. Publication in submission.

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