The performance of sonographic antenatal birth weight assessment assisted with artificial intelligence compared to that of manual examiners at term.

purposeThe aim of this study is to investigate the differences in the accuracy of sonographic antenatal fetal weight estimation at term with artificial intelligence (AI) compared to that of clinical sonographers at different levels of experience.

methodsThis is a prospective cohort study where pregnant women at term scheduled for an imminent elective cesarean section were recruited. Three independent antenatal fetal weight estimations for each fetus were blindly measured by an experienced resident physician with level I qualification from the German Society for Ultrasound in Medicine (group 1), a senior physician with level II qualification (group 2), and an AI-supported algorithm (group 3) using Hadlock formula 3. The differences between the three groups and the actual birth weight were examined with a paired t-test. A variation within 10% of birth weight was deemed accurate, and the diagnostic accuracies of both groups 1 and 3 compared to group 2 were assessed using receiver operating characteristic (ROC) curves. The association between accuracy and potential influencing factors including gestational age, fetal position, maternal age, maternal body mass index (BMI), twins, neonatal gender, placental position, gestational diabetes, and amniotic fluid index was tested with univariate logistic regression. A sensitivity analysis by inflating the estimated weights by daily 25 grams (g) gain for days between examination and birth was conducted.

results300 fetuses at a mean gestational week of 38.7 ± 1.1 were included in this study and examined on median 2 (2-4) days prior to delivery. Average birth weight was 3264.6 ± 530.7 g and the mean difference of the sonographic estimated fetal weight compared to birthweight was -203.6 ± 325.4 g, -132.2 ± 294.1 g, and -338.4 ± 606.2 g for groups 1, 2, and 3 respectively. The estimated weight was accurate in 62% (56.2%, 67.5%), 70% (64.5%, 75,1%), and 48.3% (42.6%, 54.1%) for groups 1, 2, and 3 respectively. The diagnostic accuracy measures for groups 1 and 3 compared to group 2 resulted in 55.7% (48.7%, 62.5%) and 68.6% (61.8%, 74.8%) sensitivity, 68.9% (58.3%, 78.2%) and 53.3% (42.5%, 63.9%) specificity and 0.62 (0.56, 0.68) and 0.61 (0.55, 0.67) area under the ROC curves respectively. There was no association between accuracy and the investigated variables. Adjusting for sensitivity analysis increased the accuracy to 68% (62.4%, 73.2%), 75% (69.7%, 79.8%), and 51.3% (45.5%, 57.1%), and changed the mean difference compared to birth weight to -136.1 ± 321.8 g, -64.7 ± 291.2 g, and -270.7 ± 605.2 g for groups 1, 2, and 3 respectively.

conclusionThe antenatal weight estimation by experienced specialists with high-level qualifications remains the gold standard and provides the highest precision. Nevertheless, the accuracy of this standard is less than 80% even after adjusting for daily weight gain. The tested AI-supported method exhibits high variability and requires optimization and validation before being reliably used in clinical practice.