Automated diagnosis of heart disease in patients with heart murmurs: application of a neural network technique.
This study was conducted to test a three-layered artificial neural network analysis of phonocardiogram recordings to diagnose, automatically and objectively, the condition of the heart in patients with heart murmurs. The data were recorded simultaneously in each of 49 patients with a heart murmur through eight microphones attached to the skin surface with adhesive tape, and were analysed by computer. The diagnosis was automated using a three-layered neural network technique. The neural network generated correct answers in over 70% of cases. Furthermore, about 80% of cases of two concurrent diseases were identified correctly. However, ventricular septal defects were incorrectly classified as aortic stenosis or aortic regurgitation, and patent ductus arteriosus was not diagnosed correctly. Accurate diagnoses can frequently be obtained using a neural network, but accuracy can be improved with further data accumulation.
Department of Cardiothoracic Surgery, University of Tokyo, School of Medicine, Tokyo, Japan. higux@blue.ocn.ne.jp
K
K
Higuchi
K
K
Sato
H
H
Makuuchi
A
A
Furuse
S
S
Takamoto
H
H
Takeda
D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006326:Heart Auscultation / Q000379:methods*; D006331:Heart Diseases / Q000175:diagnosis; D006337:Heart Murmurs / Q000175:diagnosis*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*