The program will be available shortly. Please check back later.
Presenters: Lars Ailo Bongo (1,2), Per Niklas Waaler(1), and Ingrid Skjæveland(3)
1 Department of Computer Science, UiT
2 SFI Visual Intelligence
3 Norinnova
Abstract: We have developed and patented a machine learning algorithm to detect valvular heart disease using heart sounds recorded in Tromsøundersøkelsen. Despite the cohort being unselected, the algorithm detected aortic stenosis with performance exceeding performance achieved in similar studies based on selected cohorts. Detection of aortic and mitral regurgitation based on heart sound audio was unreliable, but sensitivity was considerably higher for symptomatic cases, and inclusion of clinical variables improved prediction significantly.
In this seminar, we will first present the algorithm and an evaluation of its performance. Then we will describe our lessons learned patenting our research results, and our future plans for commercializing the results. Finally, we will describe how we combine protection through patents with open science principles.
Pre-print of our paper: https://www.medrxiv.org/content/10.1101/2022.11.28.22279153v1
Open source code: https://github.com/uit-hdl/heart-sound-classification
Data: https://uit.no/research/tromsostudy
Description of variables http://tromsoundersokelsen.uit.no/tromso/
In compliance with GDPR consent requirements, presentations given in a Visual Intelligence context maybe recorded with the consent of the speaker. All recordings are edited to remove all faces, names and voices of other participants. Questions and comments by the audience will hence be removed and will not appear in the recording. With the freely given consent from the speaker, recorded presentation may be posted on the Visual Intelligence YouTube channel.