Accuracy of sonographic fetal weight estimation prior to delivery in a Chinese han population

Identifying First-Trimester Risk Factors for SGA-LGA Using Weighted Inheritance Voting Ensemble Learning

The classification of fetuses as Small for Gestational Age (SGA) and Large for Gestational Age (LGA) is a critical aspect of neonatal health assessment. SGA and LGA, terms used to describe fetal weights that fall below or above the expected weights for Appropriate for Gestational Age (AGA) fetuses, indicate intrauterine growth restriction and excessive fetal growth, respectively. Early prediction and assessment of latent risk factors associated with these classifications can facilitate timely medical interventions, thereby optimizing the health outcomes for both the infant and the mother. This study aims to leverage first-trimester data to achieve these objectives. This study analyzed data from 7943 pregnant women, including 424 SGA, 928 LGA, and 6591 AGA cases, collected from 2015 to 2021 at the Third Affiliated Hospital of Sun Yat-sen University in Guangzhou, China. We propose a novel algorithm, named the Weighted Inheritance Voting Ensemble Learning Algorithm (WIVELA), to predict the classification of fetuses into SGA, LGA, and AGA categories based on biochemical parameters, maternal factors, and morbidity during pregnancy. Additionally, we proposed algorithms for relevance determination based on the classifier to ascertain the importance of features associated with SGA and LGA. The proposed classification solution demonstrated a notable average accuracy rate of 92.12% on 10-fold cross-validation over 100 loops, outperforming five state-of-the-art machine learning algorithms. Furthermore, we identified significant latent maternal risk factors directly associated with SGA and LGA conditions, such as weight change during the first trimester, prepregnancy weight, height, age, and obstetric factors like fetal growth restriction and birthing LGA baby. This study also underscored the importance of biomarker features at the end of the first trimester, including HDL, TG, OGTT-1h, OGTT-0h, OGTT-2h, TC, FPG, and LDL, which reflect the status of SGA or LGA fetuses. This study presents innovative solutions for classifying and identifying relevant attributes, offering valuable tools for medical teams in the clinical monitoring of fetuses predisposed to SGA and LGA conditions during the initial stage of pregnancy. These proposed solutions facilitate early intervention in nutritional care and prenatal healthcare, thereby contributing to enhanced strategies for managing the health and well-being of both the fetus and the expectant mother.

Predictive value and accuracy of prenatal four-dimensional color ultrasound for fetal abnormal development

To investigate the value and accuracy of prenatal GE-E10 ultrasound Equipment in predicting fetal abnormal development. 160 pregnant women and women who received prenatal ultrasound examination were selected. Before delivery, all pregnant women were examined by conventional two-dimensional and four-dimensional (4D) ultrasound. 18 fetuses with abnormal development were detected by gold standard in 160 pregnant women. Sensitivity and specificity of two-dimensional color ultrasound in diagnosing fetal abnormal development were 78.38% and 82.60%. The sensitivity and specificity of 4D color ultrasound in diagnosing fetal abnormal development were 81.15% and 83.43%. ROC showed that the AUC (0.873) of 4D color ultrasound was higher than that of two-dimensional color ultrasound (0.827). The diagnostic efficiency of 4D ultrasound is greater. The accuracy, specificity and sensitivity of 4D color ultrasound in the diagnosis of fetal abnormal development is high, and it is valuable for prenatal screening of macrosomia and low birth weight.

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.

Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod

An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.

Birth weight prediction models for the different gestational age stages in a Chinese population

The study aims to develop new birth weight prediction models for different gestational age stages using 2-dimensional (2D) ultrasound measurements in a Chinese population. 2D ultrasound was examined in pregnant women with normal singleton within 3 days prior to delivery (28–42 weeks’ gestation). A total of 19,310 fetuses were included in the study and randomly split into the training group and the validation group. Gestational age was divided into five stages: 28–30, 31–33, 34–36, 37–39 and 40–42 weeks. Multiple linear regression (MLR), fractional polynomial regression (FPR) and volume-based model (VM) were used to develop birth weight prediction model. New staged prediction models (VM for 28–36 weeks, MLR for 37–39 weeks, and FPR for 40–42 weeks) provided lower systematic errors and random errors than previously published models for each gestational age stage in the training group. The similar results were observed in the validation group. Compared to the previously published models, new staged models had the lowest aggregate systematic error (0.31%) and at least a 19.35% decrease; at least a 4.67% decrease for the root-mean-square error (RMSE). The prediction rates within 5% and 10% of birth weight for new staged models were higher than those for previously published models, which were 54.47% and 85.10%, respectively. New staged birth weight prediction models could improve the accuracy of birth weight estimation for different gestational age stages in a Chinese population.

Comparison of the accuracy of INTERGROWTH-21 formula with other ultrasound formulae in fetal weight estimation

OBJECTIVE

A new ultrasound formula for fetal weight estimation was proposed from the INTERGROWTH-21 project in 2017. There is no comparison of its accuracy with other ultrasound formulae. This study aims to compare the accuracy of INTERGROWTH-21 formula in fetal weight estimation with the traditional Hadlock1 and Shepard formula.

MATERIALS AND METHODS

All pregnant patients who had delivery in United Christian Hospital between January to December 2016 were retrospectively reviewed. Those who had prenatal ultrasound scan performed within 7 days of delivery were recruited. Hadlock1, Shepard and INTERGROWTH-21 formula were used to estimate the fetal weight and their accuracies were compared with the actual birthweight of neonates.

RESULTS

A total of 403 patients were recruited. Hadlock1 was the most accurate with the lowest mean absolute percentage error (MAPE) 7.34 when compared with Shepard (9.00; p < 0.001) and INTERGROWTH-21 (9.07; p < 0.001). INTERGROWTH-21 had the lowest proportion of patients having estimated fetal weight within 10% discrepancy from the actual birthweight (57.6%) compared with Hadlock1 (71.2%; p < 0.001) and Shepard (66.3; p = 0.011). Presence of intrauterine growth restriction (IUGR) or fetal macrosomia (>=4000 g) were both associated with significantly higher MAPE in Hadlock1 and INTERGROWTH-21. IUGR (p = 0.005) and macrosomia (p = 0.004) remained significant in the final equation of logistic regression model that affect the precision of fetal weight estimation in Hadlock1, while only IUGR was significant in INTERGROWTH-21 (p < 0.001).

CONCLUSION

INTERGROWTH-21 formula was not shown to be better than the traditional Hadlock1 or Shepard formulae. Future prospective studies would be required to evaluate the accuracy of INTERGROWTH-21 formula especially at the extremes of birthweight.