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1.	Ljungvall, Ingrid, et al. (author) Thrilling AI-A novel, signal-based digital biomarker for diagnosing canine heart diseases 2022 In: Current Directions in Biomedical Engineering. - : Walter de Gruyter GmbH. - 2364-5504. ; 8, s. 765-768 Journal article (peer-reviewed)abstract Auscultation methods enable non-invasive diagnosis of diseases, e.g. of the heart, based on heartbeat sounds. Regular, early examinations using machine learning techniques could help to detect diseases at an early stage to prevent serious health conditions and then provide optimal therapy through continuous monitoring. There is already a lot of work on human data using AI algorithms to detect patterns in signals or images. However, there is hardly no work on detecting heart murmurs with digital such as Myxomatous Mitral Valve Disease. In this paper, we present a canine auscultation project that aims to provide a tool to establish a baseline of classification parameters from audio signals that could be used to monitor canine health status by analyzing deviations from this baseline. In the future, data analysis could also lead to prediction and early detection of other diseases.

Ljungvall, Ingrid, et al. (author)
Thrilling AI-A novel, signal-based digital biomarker for diagnosing canine heart diseases
2022
In: Current Directions in Biomedical Engineering. - : Walter de Gruyter GmbH. - 2364-5504. ; 8, s. 765-768
Journal article (peer-reviewed)abstract
- Auscultation methods enable non-invasive diagnosis of diseases, e.g. of the heart, based on heartbeat sounds. Regular, early examinations using machine learning techniques could help to detect diseases at an early stage to prevent serious health conditions and then provide optimal therapy through continuous monitoring. There is already a lot of work on human data using AI algorithms to detect patterns in signals or images. However, there is hardly no work on detecting heart murmurs with digital such as Myxomatous Mitral Valve Disease. In this paper, we present a canine auscultation project that aims to provide a tool to establish a baseline of classification parameters from audio signals that could be used to monitor canine health status by analyzing deviations from this baseline. In the future, data analysis could also lead to prediction and early detection of other diseases.

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