A modified model can improve the accuracy of foetal weight estimation by magnetic resonance imaging

Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility

OBJECTIVES

To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR).

METHODS

Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived.

RESULTS

The method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10thpercentile and for 14 out of 17 FGR fetuses below the 3rd percentile. Six fetuses referred to MRI as AGA were found to be < 3rd percentile.

CONCLUSIONS

The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses.

CLINICAL RELEVANCE STATEMENT

Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction.

KEY POINTS

• An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.

Predicting fetal weight by three-dimensional limb volume ultrasound (AVol/TVol) and abdominal circumference

BACKGROUND

Fetal weight is an important parameter to ensure maternal and child safety. The purpose of this study was to use three-dimensional (3D) limb volume ultrasound combined with fetal abdominal circumference (AC) measurement to establish a model to predict fetal weight and evaluate its efficiency.

METHODS

A total of 211 participants with single pregnancy (28-42 weeks) were selected between September 2017 and December 2018 in the Beijing Obstetrics and Gynecology Hospital of Capital Medical University. The upper arm (AVol)/thigh volume (TVol) of fetuses was measured by the 3D limb volume technique. Fetal AC was measured by two-dimensional ultrasound. Nine cases were excluded due to incomplete information or the interval between examination and delivery >7 days. The enrolled 202 participants were divided into a model group (134 cases, 70%) and a verification group (68 cases, 30%) by mechanical sampling method. The linear relationship between limb volume and fetal weight was evaluated using Pearson Chi-squared test. The prediction model formula was established by multivariate regression with data from the model group. Accuracy of the model formula was evaluated with verification group data and compared with traditional formulas (Hadlock, Lee2009, and INTERGROWTH-21st) by paired t-test and residual analysis. Receiver operating characteristic curves were generated to predict macrosomia.

RESULTS

AC, AVol, and TVol were linearly related to fetal weight. Pearson correlation coefficient was 0.866, 0.862, and 0.910, respectively. The prediction model based on AVol/TVol and AC was established as follows: Y = -481.965 + 12.194TVol + 15.358AVol + 67.998AC, R2adj = 0.868. The scatter plot showed that when birth weight fluctuated by 5% (i.e., 95% to 105%), the difference between the predicted fetal weight by the model and the actual weight was small. A paired t-test showed that there was no significant difference between the predicted fetal weight and the actual birth weight (t = -1.015, P = 0.314). Moreover, the residual analysis showed that the model formula's prediction efficiency was better than the traditional formulas with a mean residual of 35,360.170. The combined model of AVol/TVol and AC was superior to the Lee2009 and INTERGROWTH-21st formulas in the diagnosis of macrosomia. Its predictive sensitivity and specificity were 87.5% and 91.7%, respectively.

CONCLUSION

Fetal weight prediction model established by semi-automatic 3D limb volume combined with AC is of high accuracy, sensitivity, and specificity. The prediction model formula shows higher predictive efficiency, especially for the diagnosis of macrosomia.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03002246; https://clinicaltrials.gov/ct2/show/NCT03002246?recrs=e&cond=fetal&draw=8&rank=67.

The use of magnetic resonance imaging in the prediction of birthweight

Extremes of fetal growth can increase adverse pregnancy outcomes, and this is equally applicable to single and multiple gestations. Traditionally, these cases have been identified using simple two-dimensional ultrasound which is quite limited by its low precision. Magnetic resonance imaging (MRI) has now been used for many years in obstetrics, mainly as an adjunct to ultrasound for congenital abnormalities and increasingly as part of the post-mortem examination. However, MRI can also be used to accurately assess fetal weight as first demonstrated by Baker et al in 1994, using body volumes rather than standard biometric measurements. This publication was followed by several others, all of which confirmed the superiority of MRI; however, despite this initial promise, the technique has never been successfully integrated into clinical practice. In this review, we provide an overview of the literature, detail the various techniques and formulas currently available, discuss the applicability to specific high-risk groups and present our vision for the future of MRI within clinical obstetrics.

Two new models for the estimation of foetal weight more than a week before delivery: An MRI study

PURPOSE

To develop and evaluate new formulas to determine the magnetic resonance imaging (MRI)-based estimated foetal weight (EFW) more than a week before delivery.

METHODS

The study included 153 women with singleton pregnancies who gave birth to live, normal neonates within 15-21 days of the MRI examination for whom foetal body volume biometry data were available at term. All foetuses were randomly divided into a testing group (102) and a validation group (51). Regression analysis was used to determine the single volume or the combination of volume and MRI-to-delivery interval that determined the EFW. The accuracy of the two new models and the primary existing model developed by Baker et al. were evaluated in validation group.

RESULTS

The two new models had similar mean percentage errors (MPEs) (3.9% vs 3.9%) and proportions of pregnancies with an MPE < 10% (92.2% vs 90.2%); the model incorporating volume and MRI-to-delivery had relatively higher proportions of pregnancies with an MPE < 5% (72.5% vs 64.7%) and EFWs in agreement with the birth weights. The error in the Baker model was almost twice that in the new models.

CONCLUSION

The accuracy of foetal weight estimation more than one week before delivery using the model developed by Baker et al. was poor and was significantly improved by the new models. A combination of the foetal body volume and MRI-to-delivery interval will enable the more accurate determination of the EFWs.