Interpretation of nonstress tests by an artificial neural network

Abstract

OBJECTIVE: Our purpose was to evaluate an artificial neural network in the interpretation of nonstress tests. STUDY DESIGN: A nonlinear artificial neural network trained by backpropagation was taught to interpret records of nonstress tests by two learning sets. The first set contained nonstress tests that were similarly interpreted by three human experts; the second set contained a subset of nonstress tests that led to interobserver disagreement. Both “raw” fetal heart rate and uterine contraction data and 17 qualified variables obtained by automated computer analysis were introduced to the input layer. After training, the network was tested by presenting it with input patterns to which it had not been exposed. The performance of the system was examined in relation to the human expert. RESULTS: After training the neural network with the first set, a sensitivity of 88.9% and a false-positive rate of 4.3% were obtained at testing. When the learning and test set contained records that led to interobserver disagreement, a sensitivity of 86.7% and a false-positive rate of 19.7% were obtained. Sixty percent of fetal heart rate records interpreted as abnormal by the neural network were interpreted likewise by the human experts. CONCLUSIONS: the results obtained are encouraging in that the neural network could discriminate between normal and abnormal nonstress tests. Further evaluation of this new technique is mandatory to evaluate its efficacy and reliability in interpreting fetal heart rate records.