Author: John S Chorba; Avi M Shapiro; Le Le; John Maidens; John Prince; Steve Pham; Mia M Kanzawa; Daniel N Barbosa; Brent E White; Jason Paek; Sophie G Fuller; Grant W Stalker; Sara A Bravo; Dina Jean; Subramaniam Venkatraman; Patrick M McCarthy; James D Thomas
Title: A Deep Learning Algorithm for Automated Cardiac Murmur Detection Via a Digital Stethoscope Platform Document date: 2020_4_3
ID: fogzjrk2_47
Snippet: The deep learning algorithm evaluated here is designed to be used as a clinician-assisted screening tool. Similar to blood test results, the algorithm can have false positive and false negative results, and therefore it is important that results of the algorithm are placed within the appropriate clinical context. We note that the murmur-detection algorithm was developed using, to our knowledge, the world's largest adult echocardiogram-paired hear.....
Document: The deep learning algorithm evaluated here is designed to be used as a clinician-assisted screening tool. Similar to blood test results, the algorithm can have false positive and false negative results, and therefore it is important that results of the algorithm are placed within the appropriate clinical context. We note that the murmur-detection algorithm was developed using, to our knowledge, the world's largest adult echocardiogram-paired heart sound recording database. Looking ahead, this database can potentially be used in the development of other clinical tools, including algorithms to differentiate between innocent and pathologic murmurs, as well as algorithms to identify specific types of VHD, such as aortic stenosis, mitral regurgitation, and tricuspid regurgitation.
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