The use of a neural network for the ultrasonographic estimation of fetal weight in the macrosomic fetus.
The error associated with regression analysis methods for the ultrasonographic estimation of fetal weight in the suspected macrosomic fetus, approximately 10%, is clinically unacceptable. This study was undertaken to evaluate the applicability of an emerging technique, biologically simulated intelligence, to this problem. One hundred patients with suspected macrosomic fetuses underwent ultrasonographic measurements of biparietal diameter, head and abdominal circumference, femur length, abdominal subcutaneous tissue, and amniotic fluid index. The biologically simulated intelligence model included gestational age, fundal height, age, gravidity, and height. The model was then compared with results obtained from previously published formulas relying on the abdominal circumference and femur length. The biologically simulated intelligence yielded an average error of 4.7% from actual birth weight, statistically better (p = 0.001) than the results obtained from regression models.
Farmer
R M
RM
Department of Obstetrics and Gynecology, University of Southern California School of Medicine, Women's Hospital, Los Angeles 90033.
Medearis
A L
AL
Hirata
G I
GI
Platt
L D
LD
D001835:Body Weight; D003198:Computer Simulation; D005260:Female; D005320:Fetal Macrosomia / Q000000981:diagnostic imaging*; D005333:Fetus / Q000033:anatomy & histology; D005865:Gestational Age; D006801:Humans; D016571:Neural Networks, Computer; D011247:Pregnancy; D014463:Ultrasonography