title: Preliminary evaluation of an intelligent system for the management of labour.
abstract: Over the past 10-15 years, workers using conventional computing approaches have attempted to provide an accurate assessment of fetal condition during labour based on the cardiotocogram (CTG) alone. These have not proved successful perhaps because the correct interpretation of fetal condition also requires physiological knowledge, specific patient information, knowledge of events during labour and considerable practical experience. An intelligent system which considers all the relevant information and embodies expertise may better diagnose fetal condition and support decision making. This study reports the preliminary evaluation of such a system and investigates whether this approach can attain a performance comparable with experienced local clinicians. From a database of 200 high risk labour records, 30 cases were selected; the 9 cases which received clinical intervention for 'fetal-distress' and a further 21 cases selected randomly. The management specified by the system, 3 experienced clinicians (A, B and C) and the actual clinical management were compared in a retrospective blinded review. The experts were found to agree well with each other. Expert A reviewed the cases five months later and was found to be entirely consistent in the management of 28 of the 30 cases. The system's actions were indistinguishable from the experts' and in no case did the system recommend an action not also recommended by at least one experienced reviewer. This study demonstrates the potential of an intelligent system to assist in the management of labour.
authors.affiliation: Plymouth Perinatal Research Group, Postgraduate Medical School, University of Plymouth, U.K.
authors.forename: R D
authors.initials: RD
authors.lastname: Keith
authors.forename: J
authors.initials: J
authors.lastname: Westgate
authors.forename: G W
authors.initials: GW
authors.lastname: Hughes
authors.forename: E C
authors.initials: EC
authors.lastname: Ifeachor
authors.forename: K R
authors.initials: KR
authors.lastname: Greene
mesh_terms: D001185:Artificial Intelligence; D036861:Delivery, Obstetric; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D005260:Female; D005312:Fetal Blood / Q000737:chemistry; D006801:Humans; D006863:Hydrogen-Ion Concentration; D007231:Infant, Newborn; D007743:Labor, Obstetric; D008991:Monitoring, Physiologic / Q000379:methods*; D009774:Obstetrics; D011247:Pregnancy; D011256:Pregnancy Outcome; D011295:Prenatal Care / Q000379:methods; D012189:Retrospective Studies