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_29
Snippet: Filtering only the good signal recordings, we directly compared prediction to clinically defined ground truth in the confusion matrix in Table 2 . Algorithm performance had a sensitivity and specificity for detecting murmurs of 76.3% (95% CI: 72.9-79.3%) and 91.4% (95% CI: 89.6-93.1%), respectively (Table 3) , and a positive predictive value of 86.6% (95% CI: 84.0-89.3%) using the murmur prevalence from this test set. The likelihood ratios positi.....
Document: Filtering only the good signal recordings, we directly compared prediction to clinically defined ground truth in the confusion matrix in Table 2 . Algorithm performance had a sensitivity and specificity for detecting murmurs of 76.3% (95% CI: 72.9-79.3%) and 91.4% (95% CI: 89.6-93.1%), respectively (Table 3) , and a positive predictive value of 86.6% (95% CI: 84.0-89.3%) using the murmur prevalence from this test set. The likelihood ratios positive and negative are 8.89 (95% CI: 7.35-11.08) and 0.259 (95% CI: 0.225-0.297), respectively (Table 3) .
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