OBJECTIVE: Computer-aided auscultation in the differentiation of pathologic (AHA class I) from no or innocent murmurs (AHA class III) would be of great value to the general practitioner. This would allow objective screening for structural heart disease, standardized documentation of auscultation findings, and may avoid unnecessary referrals to pediatric cardiologists. Our goal was to assess the quality of a novel computerized algorithm that automatically classifies murmurs in phonocardiograms (PCGs) acquired in a pediatric population. DESIGN: This is a pilot study testing the ability of a novel computerized algorithm to accurately diagnose PCGs compared with interpreted echocardiograms as a gold standard. SETTING: This study was performed in pediatric cardiology clinics at a tertiary care hospital. PATIENTS: All incoming patients were recruited, including patients with no murmurs, innocent murmurs, and pathologic murmurs (106 patients). INTERVENTION: Using an electronic stethoscope, PCGs were acquired by the pediatric cardiologist from each patient. The PCGs were analyzed by the algorithm and diagnoses were compared with findings by echocardiograms interpreted by pediatric cardiologists which were used as the gold standard. OUTCOME MEASURES: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. RESULTS: When compared with echocardiography as a gold standard in diagnosing murmurs, the computerized algorithm tested on N=34 PCGs, yielded a sensitivity of 87% and specificity of 100%, a positive predictive value of 100%, negative predictive value of 90% and an accuracy of 94%. CONCLUSION: With echocardiogram as a gold standard, this computerized algorithm can detect pathologic murmurs with high sensitivity, specificity and accuracy, comparable to if not better than published results of pediatric cardiologists and neonatologists. This study confirms the high quality and "real-world" robustness of a novel computational algorithm in the assessment of pediatric murmurs.
OBJECTIVE: Computer-aided auscultation in the differentiation of pathologic (AHA class I) from no or innocent murmurs (AHA class III) would be of great value to the general practitioner. This would allow objective screening for structural heart disease, standardized documentation of auscultation findings, and may avoid unnecessary referrals to pediatric cardiologists. Our goal was to assess the quality of a novel computerized algorithm that automatically classifies murmurs in phonocardiograms (PCGs) acquired in a pediatric population. DESIGN: This is a pilot study testing the ability of a novel computerized algorithm to accurately diagnose PCGs compared with interpreted echocardiograms as a gold standard. SETTING: This study was performed in pediatric cardiology clinics at a tertiary care hospital. PATIENTS: All incoming patients were recruited, including patients with no murmurs, innocent murmurs, and pathologic murmurs (106 patients). INTERVENTION: Using an electronic stethoscope, PCGs were acquired by the pediatric cardiologist from each patient. The PCGs were analyzed by the algorithm and diagnoses were compared with findings by echocardiograms interpreted by pediatric cardiologists which were used as the gold standard. OUTCOME MEASURES: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. RESULTS: When compared with echocardiography as a gold standard in diagnosing murmurs, the computerized algorithm tested on N=34 PCGs, yielded a sensitivity of 87% and specificity of 100%, a positive predictive value of 100%, negative predictive value of 90% and an accuracy of 94%. CONCLUSION: With echocardiogram as a gold standard, this computerized algorithm can detect pathologic murmurs with high sensitivity, specificity and accuracy, comparable to if not better than published results of pediatric cardiologists and neonatologists. This study confirms the high quality and "real-world" robustness of a novel computational algorithm in the assessment of pediatric murmurs.
Authors: Ryan L Melvin; Matthew G Broyles; Elizabeth W Duggan; Sonia John; Andrew D Smith; Dan E Berkowitz Journal: Front Digit Health Date: 2022-04-25
Authors: Manuel A Soto-Murillo; Jorge I Galván-Tejada; Carlos E Galván-Tejada; Jose M Celaya-Padilla; Huizilopoztli Luna-García; Rafael Magallanes-Quintanar; Tania A Gutiérrez-García; Hamurabi Gamboa-Rosales Journal: Healthcare (Basel) Date: 2021-03-12
Authors: John P Birrane; Zi Lun Lim; Chee H Liew; Liesbeth Rosseel; Adrienne Heerey; Kieran Coleman; Joseph Gallagher; Darren Mylotte; John W McEvoy Journal: Ir J Med Sci Date: 2021-04-24 Impact factor: 1.568