Automated Fractional Limb Volume Measurements Improve the Precision of Birth Weight Predictions in Late Third-Trimester Fetuses

The NICHD Fetal 3D Study: A Pregnancy Cohort Study of Fetal Body Composition and Volumes

There's a paucity of robust normal fractional limb and organ volume standards from a large and diverse ethnic population. The Fetal 3D Study was designed to develop research and clinical applications for fetal soft tissue and organ volume assessment. The NICHD Fetal Growth Studies (2009-2013) collected 2D and 3D fetal volumes. In the Fetal 3D Study (2015-2019), sonographers performed longitudinal 2D and 3D measurements for specific fetal anatomical structures in research ultrasounds of singletons and dichorionic twins. The primary aim was to establish standards for fetal body composition and organ volumes, overall and by maternal race/ethnicity, and determine whether these standards vary for twins versus singletons. We describe the study design, methods, and details about reviewer training. Basic characteristics of this cohort, with their corresponding distributions of fetal 3D measurements by anatomical structure, are summarized. This investigation is responsive to critical data gaps in understanding serial changes in fetal subcutaneous fat, lean body mass, and organ volume in association with pregnancy complications. In the future, this cohort can answer critical questions regarding the potential influence of maternal characteristics, lifestyle factors, nutrition, and biomarker and chemical data on longitudinal measures of fetal subcutaneous fat, lean body mass, and organ volumes.

A Clinical Approach to Semiautomated Three-Dimensional Fetal Brain Biometry-Comparing the Strengths and Weaknesses of Two Diagnostic Tools: 5DCNS+TM and SonoCNSTM

(1) Objective: We aimed to evaluate the accuracy and efficacy of AI-assisted biometric measurements of the fetal central nervous system (CNS) by comparing two semiautomatic postprocessing tools. We further aimed to discuss the additional value of semiautomatically generated sagittal and coronal planes of the CNS. (2) Methods: Three-dimensional (3D) volumes were analyzed with two semiautomatic software tools, 5DCNS+™ and SonoCNS™. The application of 5DCNS+™ results in nine planes (axial, coronal and sagittal) displayed in a single template; SonoCNS™ depicts three axial cutting sections. The tools were compared regarding automatic biometric measurement accuracy. (3) Results: A total of 129 fetuses were included for final analysis. Our data indicate that, in terms of the biometric quantification of head circumference (HC), biparietal diameter (BPD), transcerebellar diameter (TCD) and cisterna magna (CM), the accuracy of SonoCNS™ was higher with respect to the manual measurement of an experienced examiner compared to 5DCNS+™, whereas it was the other way around regarding the diameter of the posterior horn of the lateral ventricle (Vp). The inclusion of four orthogonal coronal views in 5DCNS+™ gives valuable information regarding spatial arrangements, particularly of midline structures. (4) Conclusions: Both tools were able to ease assessment of the intracranial anatomy, highlighting the additional value of automated algorithms in clinical use. SonoCNS™ showed a superior accuracy of plane reconstruction and biometry, but volume reconstruction using 5DCNS+™ provided more detailed information, which is needed for an entire neurosonogram as suggested by international guidelines.

A unified representation of control logic in human-ultrasound machine interaction

Advances in human-computer interaction (HCI) technologies have granted sonographers and radiologists a much improved user experience when operating different ultrasound (US) machines. Continued HCI improvements in US would benefit from a systematic study of the HCI control logic used in this domain. Such a study has not been presented previously and is the subject of this paper. We surveyed sonographers to determine the most frequently used controls in US machines. We standardized the representation of the US machine HCI control logic by using the unified modelling language (UML). We used UML diagrams to analyze the HCI control logic of 10 different cart-based US machines from several major manufacturers, and we discovered that the control logic for the most frequently used functions are identical. While this control logic does not follow an established standard, it has been commonly adopted. Using the UML for the visualization and formulation of control logic, we can target logically optimal interactions (whose operation steps cannot be further reduced), e.g., adjustment of B-mode gain, frequency and depth, and can derive methods to simplify logically sub-optimal interactions, e.g., the pointing and selecting operation, as well as image measurements. Our study provides insights into existing HCI approaches used in US machines and establishes a rigorous UML-based framework for future US machine design to improve interoperability, efficiency and ease-of-use.

Semiautomatic Assessment of Fetal Fractional Limb Volume for Weight Prediction in Clinical Praxis: How Does It Perform in Routine Use?

OBJECTIVES

Semiautomatic fractional limb volume (FLV) models have recently produced promising results for fetal birth weight (BW) estimation. We tested those models in a more unselected population hypothesizing that the FLV models would improve accuracy and precision of fetal BW estimation compared to the Hadlock model.

METHODS

We compared the performance of different BW prediction models: Hadlock (biparietal diameter [BPD], abdominal circumference (AC), femur diaphysis length) and modified Lee thigh volume (TVol) and arm volume (AVol) (BPD, AC, automated fractional TVol, and AVol). Accuracy (systematic errors, mean percent differences) and precision (random errors, ± 1 SD of percent differences) were calculated.

RESULTS

A total of 75 fetuses were included for final analysis. The Hadlock model showed the most consistent results with accurate BW estimation not significantly different from zero (-0.37 ± 8.53%). The modified fractional thigh and arm volume models were less accurate but trended toward more precise results (-2.63 ± 7.69% and -3.85 ± 7.47%, respectively). In addition, the modified TVol model showed the trend to predict more BWs within ±10% of the actual BW compared to the Hadlock model (81.3 versus 74.67%, ns).

CONCLUSIONS

Based on our results, fetal weight estimation using the modified semiautomatic FLV models generates less accurate results in third-trimester fetuses compared to the Hadlock model. Those models recently published might improve the results of BW prediction by showing a higher precision than conventional models, especially in small and large fetuses. Further studies are needed to investigate the clinical usefulness of the new models.

The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play

The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.

Soft tissue assessment for fetal growth restriction

Contemporary clinical practice heavily relies on interpretation of population-based birth weight standards to evaluate neonatal nutrition status. Obstetricians have adopted the use of estimated fetal weight in a similar manner to estimate fetal nutritional status. However, most fetal weight prediction models overemphasize skeletal parameters such as biparietal diameter, head circumference, and femur diaphysis length. Although most EFW calculations also include abdominal circumference, this 2D growth parameter is largely defined by liver size and a small rim of subcutaneous fat. Advances in 3D ultrasound imaging and the development of more robust image analysis tools have now made it possible to reliably add a soft tissue component for fetal nutritional assessment. This chapter explains why fetal soft tissue evaluation is clinically relevant, describes different techniques for evaluating these sonographic parameters, and outlines future directions for their practical utility in the care of malnourished fetuses.

Fetal weight estimation by automated three-dimensional limb volume model in late third trimester compared to two-dimensional model: a cross-sectional prospective observational study

Background

Accurate estimation of fetal weight is important for prenatal care and for detection of fetal growth abnormalities. Prediction of fetal weight entails the indirect measurement of fetal biometry by ultrasound that is then introduced into formulae to calculate the estimated fetal weight. The aim of our study was to evaluate the accuracy of fetal weight estimation of Chinese fetuses in the third trimester using an automated three-dimensional (3D) fractional limb volume model, and to compare this model with the traditional two-dimensional (2D) model.

Methods

Prospective 2D and 3D ultrasonography were performed among women with singleton pregnancies 7 days before delivery to obtain 2D data, including fetal biparietal diameter, abdominal circumference and femur length, as well as 3D data, including the fractional arm volume (AVol) and fractional thigh volume (TVol). The fetal weight was estimated using the 2D model and the 3D fractional limb volume model respectively. Percentage error was defined as (estimated fetal weight - actual birth weight) divided by actual birth weight and multiplied by 100. Systematic errors (accuracy) were evaluated as the mean percentage error (MPE). Random errors (precision) were calculated as ±1 SD of percentage error. The intraclass correlation coefficient (ICC) was used to analyze the inter-observer reliability of the 3D ultrasound measurements of fractional limb volume.

Results

Ultrasound examination was performed on 56 fetuses at 39.6 ± 1.4 weeks’ gestation. The average birth weight of the newborns was 3393 ± 530 g. The average fetal weight estimated by the 2D model was 3478 ± 467 g, and the MPE was 3.2 ± 8.9. The average fetal weights estimated by AVol and TVol of the 3D model were 3268 ± 467 g and 3250 ± 485 g, respectively, and the MPEs were − 3.3 ± 6.6 and − 3.9 ± 6.1, respectively. For the 3D TVol model, the proportion of fetuses with estimated error ≤ 5% was significantly higher than that of the 2D model (55.4% vs. 33.9%, p < 0.05). For fetuses with a birth weight < 3500 g, the accuracy of the AVol and TVol models were better than the 2D model (− 0.8 vs. 7.0 and − 2.8 vs. 7.0, both p < 0.05). Moreover, for these fetuses, the proportions of estimated error ≤ 5% of the AVol and TVol models were 58.1 and 64.5%, respectively, significantly higher than that of the 2D model (19.4%) (both p < 0.05). The inter-observer reliability of measuring fetal AVol and TVol were high, with the ICCs of 0.921 and 0.963, respectively.

Conclusion

In this cohort, the automated 3D fractional limb volume model improves the accuracy of weight estimation in most third-trimester fetuses. Prediction accuracy of the 3D model for neonatal BW, particularly < 3500 g was higher than that of the traditional 2D model.

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.

Prediction of Birth Weight and Neonatal Adiposity Using Ultrasound Assessment of Soft Tissue Parameters in Addition to Two-Dimensional Conventional Biometry

INTRODUCTION

We aim to evaluate the supplementary predictive value of soft tissue markers, including fetal limb volumes, for fetal birth weight and fat tissue weight.

METHODS

This is a prospective study of 60 patients undergoing term induction of labor. Ultrasound was performed 48 h before birth, and 2D sonographic measurements, subcutaneous tissue thickness, and 3D fetal limb volumes were taken. Birth weight and neonatal fat weight were assessed by plethysmography. Clinical data were collected. The relation between ultrasound and neonatal outcomes was assessed by univariate and multivariate predictive models. The estimated and actual birth weights were compared applying different published formulas, and systematic and random error were collected and compared.

RESULTS

3D fetal limb volumes showed a strong relation to birth weight, absolute weight, and relative fat weight. The Lee 6 formula performed better than either Hadlock 3 or Lee 3 with the lowest random error. Fractional limb volumes involve a highly reproducible technique, with excellent correlation (intra-class coefficient >0.90) for both inter- and intra-observer reliability. The prevalence of estimated EFW measures within 10% error from the actual birth weight was 71.7% with the Hadlock 3 model and 95.0% with the Lee 6 model (p = 0.09).

CONCLUSION

Late assessment of 3D fetal limb volume in upper and lower extremities is not only useful for accurately predicting birth weight but is a useful marker for prediction of birth fat tissue weight.

Fetal Weight Estimation Using Automated Fractional Limb Volume With 2-Dimensional Size Parameters in Diabetic Pregnancies

OBJECTIVES

To examine the effect of adding automated fetal fractional limb volume (FLV) with conventional 2-dimensional (2D) fetal weight estimation procedures in a cohort of diabetic pregnancies.

METHODS

A pilot study of diabetic pregnancies measured standard fetal biometry within 7 days of delivery. Fractional arm volume (AVol) and fractional thigh volume (TVol) soft tissue parameters were measured with a commercially available automated software utility (5D Limb Vol; Samsung Medison Co, Ltd, Seoul, Korea). Three conventional weight prediction models that included only 2D size parameters were compared to FLV models that included AVol or TVol. Estimated and actual birth weight (BW) were assessed for the mean percent difference ± standard deviation of the percent differences. Systematic errors were evaluated by the Student t test, and random errors were compared by the Pitman test for correlated variances. The proportion of neonates with estimated fetal weight within 10% of BW was compared between groups by the McNemar test.

RESULTS

Ninety gravid women were enrolled with pregestational (26.7%) or gestational (73.3%) diabetes. All prediction models were accurate, with the exception of small underestimations by the model of Schild et al (-3.8%; Ultrasound Obstet Gynecol 2004; 23:30-35). Random errors for the AVol (6.2%) and TVol (6.9%) models were significantly more precise than the other 3 prediction models: Hadlock et al (7.8%; Am J Obstet Gynecol 1985; 151:333-337), INTERGROWTH-21st (8.0%; Ultrasound Obstet Gynecol 2017; 49:478-486), and Schild et al (8.6%; P < .01). The greatest proportion of cases with BW ±10% was also classified by the AVol (91.1%) and TVol (91.1%) models, followed by Hadlock (83.3%), INTERGROWTH-21st (78.9%), and Schild (76.7%) predictions.

CONCLUSIONS

The addition of automated FLV measurements to conventional 2D biometry was associated with improved weight predictions in this cohort of diabetic pregnancies.

Newer Insights Into Fetal Growth and Body Composition

Based on epidemiological and experimental evidence, the origins of childhood obesity and early onset metabolic syndrome can be extended back to developmental processes during intrauterine life. It is necessary to actively investigate antecedent conditions that affect fetal growth by developing reliable measures to identify variations in fetal fat deposition and body composition. Recently, the resolution of ultrasonography has remarkably improved, which enables better tissue characterization and quantification of fetal fat accumulation. In addition, fetal fractional limb volume has been introduced as a novel measure to quantify fetal soft tissue volume, including fat mass and lean mass. Detecting extreme variations in fetal fat deposition may provide further insights into the origins of altered fetal body composition in pathophysiological conditions (i.e., fetal growth restriction or fetal macrosomia), which are predisposed to the metabolic syndrome in later life. Further studies are warranted to determine the maternal or placental factors that affect fetal fat deposition and body composition. Elucidating these factors may help develop clinical interventions for altered fetal growth and body composition, which could potentially lead to primary prevention of the future risk of metabolic dysfunction.

Fetal Weight Estimation Using Automated Fractional Limb Volume With 2-Dimensional Size Parameters: A Multicenter Study

OBJECTIVES

To develop new fetal weight prediction models using automated fractional limb volume (FLV).

METHODS

A prospective multicenter study measured fetal biometry within 4 to 7 days of delivery. Three-dimensional data acquisition included the automated FLV that was based on 50% of the humerus diaphysis (fractional arm volume [AVol]) or 50% of the femur diaphysis (fractional thigh volume [TVol]) length. A regression analysis provided population sample-specific coefficients to develop 4 weight estimation models. Estimated and actual birth weights (BWs) were compared for the mean percent difference ± standard deviation of the percent differences. Systematic errors were analyzed by the Student t test, and random errors were compared by the Pitman test.

RESULTS

A total of 328 pregnancies were scanned before delivery (BW range, 825-5470 g). Only 71.3% to 72.6% of weight estimations were within 10% of actual BW using original published models by Hadlock et al (Am J Obstet Gynecol 1985; 151:333-337) and INTERGROWTH-21st (Ultrasound Obstet Gynecol 2017; 49:478-486). All predictions were accurate by using sample-specific model coefficients to minimize bias in making these comparisons (Hadlock, 0.4% ± 8.7%; INTERGROWTH-21st, 0.5% ± 10.0%; AVol, 0.3% ± 7.4%; and TVol, 0.3% ± 8.0%). Both AVol- and TVol-based models improved the percentage of correctly classified BW ±10% in 83.2% and 83.9% of cases, respectively, compared to the INTERGROWTH-21st model (73.8%; P < .01). For BW of less than 2500 g, all models slightly overestimated BW (+2.0% to +3.1%). For BW of greater than 4000 g, AVol (-2.4% ± 6.5%) and TVol (-2.3% ± 6.9%) models) had weight predictions with small systematic errors that were not different from zero (P > .05). For these larger fetuses, both AVol and TVol models correctly classified BW (±10%) in 83.3% and 87.5% of cases compared to the others (Hadlock, 79.2%; INTERGROWTH-21st, 70.8%) although these differences did not reach statistical significance.

CONCLUSIONS

In this cohort, the inclusion of automated FLV measurements with conventional 2-dimensional biometry was generally associated with improved weight predictions.

Ultrasound measurement of fetal abdominal subcutaneous tissue thickness as a predictor of large versus small fetuses for gestational age

Background

Commonly used ultrasound fetal weight estimation formulas show variable degrees of error which is more evident in fetuses with nutritional and metabolic issues; better accuracy of fetal weight estimation can be obtained by incorporation of fetal soft tissue parameters like the fetal subcutaneous tissue in the weight estimation process. The aim of this study was to assess the accuracy of fetal abdominal subcutaneous tissue thickness (FASTT) as an indicator of fetal birth weight.

Results

FASTT showed a high significant statistical correlation with fetal birth weight (r = 0.94, P value = 0.00); it showed higher sensitivity for large for gestational age (LGA) than small for gestational age (SGA) (90.9% and 86.9%, respectively). The best cutoff value for the detection of LGA was ≥ 9.2 mm and ≤ 4.5 for SGA. FASTT showed lower accuracy than abdominal circumference (AC) as an indicator of LGA (92% versus 96%, respectively). Used alone, FASTT is less sensitive than Hadlock formula in both LGA and SGA (90.9% versus 94.5% in LGA and 86.9% versus 88.9% for SGA, respectively). There was no statistical correlation between FASTT and mode of delivery (r = 0.09, P value = 0.23) nor fetal gender (r = 0.15, P value = 0.11)

Conclusion

FASTT is a good indicator of fetal birth weight especially LGA, yet it is less sensitive than AC in the prediction of LGA. It cannot be used as a predictor of mode of delivery and not affected by fetal gender.

The World Health Organization fetal growth charts: concept, findings, interpretation, and application

Ultrasound biometry is an important clinical tool for the identification, monitoring, and management of fetal growth restriction and development of macrosomia. This is even truer in populations in which perinatal morbidity and mortality rates are high, which is a reason that much effort is put onto making the technique available everywhere, including low-income societies. Until recently, however, commonly used reference ranges were based on single populations largely from industrialized countries. Thus, the World Health Organization prioritized the establishment of fetal growth charts for international use. New fetal growth charts for common fetal measurements and estimated fetal weight were based on a longitudinal study of 1387 low-risk pregnant women from 10 countries (Argentina, Brazil, Democratic Republic of Congo, Denmark, Egypt, France, Germany, India, Norway, and Thailand) that provided 8203 sets of ultrasound measurements. The participants were characterized by median age 28 years, 58% nulliparous, normal body mass index, with no socioeconomic or nutritional constraints (median caloric intake, 1840 calories/day), and had the ability to attend the ultrasound sessions, thus essentially representing urban populations. Median gestational age at birth was 39 weeks, and birthweight was 3300 g, both with significant differences among countries. Quantile regression was used to establish the fetal growth charts, which also made it possible to demonstrate a number of features of fetal growth that previously were not well appreciated or unknown: (1) There was an asymmetric distribution of estimated fetal weight in the population. During early second trimester, the distribution was wider among fetuses <50th percentile compared with those above. The pattern was reversed in the third trimester, with a notably wider variation >50th percentile. (2) Although fetal sex, maternal factors (height, weight, age, and parity), and country had significant influence on fetal weight (1-4.5% each), their effect was graded across the percentiles. For example, the positive effect of maternal height on fetal weight was strongest on the lowest percentiles and smallest on the highest percentiles for estimated fetal weight. (3) When adjustment was made for maternal covariates, there was still a significant effect of country as covariate that indicated that ethnic, cultural, and geographic variation play a role. (4) Variation between populations was not restricted to fetal size because there were also differences in growth trajectories. (5) The wide physiologic ranges, as illustrated by the 5th-95th percentile for estimated fetal weight being 2205-3538 g at 37 weeks gestation, signify that human fetal growth under optimized maternal conditions is not uniform. Rather, it has a remarkable variation that largely is unexplained by commonly known factors. We suggest this variation could be part of our common biologic strategy that makes human evolution extremely successful. The World Health Organization fetal growth charts are intended to be used internationally based on low-risk pregnancies from populations in Africa, Asia, Europe, and South America. We consider it prudent to test and monitor whether the growth charts' performance meets the local needs, because refinements are possible by a change in cut-offs or customization for fetal sex, maternal factors, and populations. In the same line, the study finding of variations emphasizes the need for carefully adjusted growth charts that reflect optimal local growth when public health issues are addressed.

Individualized growth assessment: conceptual framework and practical implementation for the evaluation of fetal growth and neonatal growth outcome

Fetal growth abnormalities can pose significant consequences on perinatal morbidity and mortality of nonanomalous fetuses. The most widely accepted definition of fetal growth restriction is an estimated fetal weight less than the 10th percentile for gestational age according to population-based criteria. However, these criteria do not account for the growth potential of an individual fetus, nor do they effectively separate constitutionally small fetuses from ones that are malnourished. Furthermore, conventional approaches typically evaluate estimated fetal weight at a single time point, rather than using serial scans, to evaluate growth. This article provides a conceptual framework for the individualized growth assessment of a fetus/neonate based on measuring second-trimester growth velocity of fetal size parameters to estimate growth potential. These estimates specify size models that generate individualized third-trimester size trajectories and predict birth characteristics. Comparisons of measured and predicted values are used to separate normally growing fetuses from those with growth abnormalities. This can be accomplished with individual anatomical parameters or sets of parameters. A practical and freely available software (Individualized Growth Assessment Program) has been developed to allow implementation of this approach for clinical and research purposes.