| Abstract | This article presents the development of an expert system for the interpretation of fetal scalp acid-base
status. The system consists of logistic transformations, back-propagation neural networks and decision
algorithms connected in series. It checks for out-of-range errors and the physiological coherence between
measurements. It then determines whether acidosis should be diagnosed, and if so, whether it is more likely
to be metabolic, respiratory or mixed. It will also flag those cases where it is difficult to interpret the data in
physiological terms. The system was tested on a database of 2174 scalp blood samples collected at the
Queens Medical Centre, Nottingham. Of these 88 samples were rejected as erroneous; 13 because of an
out-of-range pH alone (> or = 7.48); 73 because more than one measurement was marginally out of range,
and two because the relationship between measurements did not make sense. A total of 527 cases (24.2%)
were diagnosed as being acidotic; of these, 139 were respiratory, 114 mixed and 274 metabolic. We were
unable to fault the system's interpretation when the cases at the margins between diagnostic categories were
reviewed clinically. |