The use of a neural network to recognize placental insufficiency from blood flow velocity waveforms in the umbilical cord
Year: 2005
Type: dissertation
Abstract: Present-day obstetric decision-making is based on measuring the umbilical arterial blood flow velocity waveforms from one site of the cord. There is an ongoing debate on the predictive value of Doppler measurements in the evaluation of the foetal condition. The aim of this thesis is to investigate the use ofa neural network to recognise blood flow waveform shape patterns associated with placental insufficiency. Eleven backpropagation neural networks have been developed and trained based on the waveforms that are generated from the foetal mathematical model (developed in previous research) at both ends of the cord. Only two networks trained successfully. These two networks are the Levenberg-Marquardt algorithm (Trainlm) and the resilient backpropagation algorithm (Trainrp).
Author: Alkathafi Ali Alhamud
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2587454737