title: Development, evaluation and validation of an intelligent system for the management of labour.
abstract: Over the past 4 years our group has developed a prototype intelligent system which applies captured expert knowledge to support clinical decision-making during labour. This chapter presents a review of the system and the progress made to date. The system classifies the same features from the CTG as experienced clinicians using numerical algorithms and a small neural network. This hybrid approach has been shown to obtain a comparable performance with experts. The CTG information, together with the patient information and labour events, are collectively passed to an expert system for processing. The expert system interprets this combined data using a database of over 400 rules which are used to recommend action. Importantly, as the knowledge is rule-based, it allows the system to explain the reasoning which led it to recommend a certain action. In this way, the clinician is not expected to blindly follow the system's recommendations but can reach an informed judgement in the same way they might by discussing the case with an experienced informed colleague. After two internal evaluations had found the system obtained a performance comparable with local experts, an extensive external validation was undertaken. This study involved 17 experts from 16 leading centres within the UK. Each expert and the system reviewed 50 cases twice, at least one month apart which contained those CTGs considered most difficult to interpret selected from a database of 2400 high-risk labours. This study found that the majority of experts agreed well and were consistent in their management of the cases. The system obtained a performance that was indistinguishable from the experts, except it was more consistent, even when used by an engineer with little knowledge of labour management. This study demonstrates the potential for intelligent systems to transform the cardiotocograph from a difficult-to-use, ineffective recorder of fetal heart rate, to an interactive and effective decision support tool capable of raising the skills of staff.
authors: Perinatal Research Group, Postgraduate Medical School, University of Plymouth, Derriford Hospital, UK.
authors: R D
authors: RD
authors: Keith
authors: K R
authors: KR
authors: Greene
mesh_terms: D000465:Algorithms; D015148:Cardiotocography; D003661:Decision Support Techniques; D005103:Expert Systems; D005260:Female; D005316:Fetal Distress / Q000175:diagnosis* / Q000453:epidemiology; D006801:Humans; D007743:Labor, Obstetric; D015337:Multicenter Studies as Topic; D016571:Neural Networks, Computer; D009721:Nurse Midwives; D009774:Obstetrics / Q000379:methods*; D011247:Pregnancy; D011256:Pregnancy Outcome / Q000453:epidemiology; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012189:Retrospective Studies