Predicting the duration of the first stage of spontaneous labor using a neural network

To create a neural network that predicts the length of the first stage of term labor. Two hundred patients with gestations > or = 36 weeks, in spontaneous active labor are the study group: 159 for training and 41 for testing; 4 training set patients had second-stage cesarean section for obstructed labor. The network is designed with Brainmaker MacIntosh 1.0 (California Scientific Software). Inputs are uterine activity, estimated fetal weight, position, station, and gestational age; maternal parity, age, height, weight, membrane status, and cervical dilatation. Actual first stages are regressed on those predicted by the network or by a standard partogram set. Differences between actual first stage lengths and those predicted by the neural network or partogram are compared with t-tests; while the proportions of first stages accurately predicted within 1 or 2 h are compared for both methods with chi-square tests. The network trained in 4 h (1388 runs) to a 0.15 tolerance. The network predictions have significantly higher correlation (r = 0.88) than do standard partograms (r = 0.35) with actual first stage durations. Mean differences between predicted and actual first stages are significantly lower for network output than with partograms; these differences increased with first stages exceeding 3 h; 100% of trained network values are within 2 h of actual first stage length. The network performs similarly for a new set of 41 previously unseen labors. This neural network predicts the length of the first stage of spontaneous labor and uses inputs readily available to obstetricians. It outperforms typical partograms for estimating this important feature of normal labor. Future application for intrapartum prognosis could be based on this successful design.