Quality control of ultrasound for fetal biometry: results from the INTERGROWTH‐21<sup>st</sup> Project

Cavallaro, A; Ash, Stephen; Napolitano, R.; Wanyonyi, Sikolia; Ohuma, Eric O; Molloholli, M.; Sande, Joyce; Sarris, Ippokratis; Ioannou, Christos; Norris, T.; Donadono, V.; Carvalho, Maria; Purwar, Manorama; Barros, Fernando C.; Jaffer, Y A; Bertino, Enrico; Pang, Ruoming; Gravett, Michael G.; Salomon, Laurent; Noble, J. Alison; Altman, Douglas G.; Papageorghiou, Aris T.

doi:10.1002/uog.18811

Cited by 53 publications

(61 citation statements)

References 51 publications

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“…The estimated SDs of errors in AC, HC and FL obtained from Cavallaro et al 21 , using the method described in Appendix S1, were 6.93 mm for AC, 5.15 mm for HC and 1.38 mm for FL. The corresponding Gaussian distributions of these errors in measurements are shown in Figure 1; FL has the smallest spread and AC has the largest spread.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of SD units, which allow for a fair comparison of the three measures, errors in AC have the largest impact on EFW percentile and errors in HC have the smallest impact on EFW percentile. Table 2 shows the performance of screening for SGA and LGA neonates for various percentile cut-offs, when EFW is obtained using AC, HC and FL subject to random Gaussian errors with means of 0 mm and SD according to Cavallaro et al 21 . Assuming errors according to such distributions, when the 10 th and 90 th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 78.0% at false-positive rates of 4.7%.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…A multicenter study reported on measurements of fetal AC, HC and FL for 20 313 ultrasound images obtained prospectively from 4321 fetuses at 14-41 weeks' gestation; fetal AC, HC and FL were measured in a blinded fashion in triplicate on separately generated images 21 . We used data from this study 21 , which gives 95% limits of agreement for interobserver biometric measurements, to produce estimates of the standard deviations (SD) of individual measurement errors from the true dimension, as described in Appendix S1. Assuming a Gaussian distribution, we chose to examine the effects of errors of ± 0.67 and ± 1.64 SD; under the assumption of a Gaussian distribution, errors with magnitude greater than 0.67 SD occur in 50% of measurements and those with magnitude greater than 1.64 occur in 10% of measurements.…”

Section: Methodsmentioning

confidence: 99%

“…For each of the true percentiles, we obtained the gestational age for which the median values of AC, HC and FL 20 gave the EFW corresponding to the true percentile as described above for the 10 th and 90 th percentiles. We then added random errors from an independent Gaussian distribution with SDs obtained from Cavallaro et al 21 to create a sample of 100 000 observed EFWs and computed the proportion that were < 10 th and > 90 th percentiles at 36 + 0 weeks. To explore the effect of improving measurement precision, we present results using the SDs obtained from the study of Cavallaro et al 21 and for these SDs reduced to 50% of their original value.…”

Section: Methodsmentioning

confidence: 99%

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Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Wright

Smith³

et al. 2020

Ultrasound in Obstet & Gyne

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CONTRIBUTION What are the novel findings of this work?This study examines the impact of measurement errors in fetal head circumference, abdominal circumference and femur length on sonographic estimated fetal weight (EFW) and their effect on the prediction of small-(SGA) and large-(LGA) for-gestational-age fetuses.What are the clinical implications of this work? Measurement error in fetal biometry causes substantial error in EFW, resulting in misclassification of SGA and LGA fetuses. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts on the prediction of SGA and LGA fetuses. ABSTRACTObjectives First, to obtain measurement-error models for biometric measurements of fetal abdominal circumference (AC), head circumference (HC) and femur length (FL), and, second, to examine the impact of biometric measurement error on sonographic estimated fetal weight (EFW) and its effect on the prediction of small-(SGA) and large-(LGA) for-gestational-age fetuses with EFW < 10 th and > 90 th percentile, respectively.Methods Measurement error standard deviations for fetal AC, HC and FL were obtained from a previous large study on fetal biometry utilizing a standardized measurement protocol and both qualitative and quantitative quality-control monitoring. Typical combinations of AC, HC and FL that gave EFW on the 10 th and 90 th percentiles were determined. A Monte-Carlo simulation study was carried out to examine the effect of measurement error on the classification of fetuses as having EFW above or below the 10 th and 90 th percentiles. ResultsErrors were assumed to follow a Gaussian distribution with a mean of 0 mm and SDs, obtained from a previous well-conducted study, of 6.93 mm for AC, 5.15 mm for HC and 1.38 mm for FL. Assuming errors according to such distributions, when the 10 th and 90 th percentiles are used to screen for SGA and LGA fetuses, respectively, the detection rates would be 78.0% at false-positive rates of 4.7%. If the cut-offs were relaxed to the 30 th and 70 th percentiles, the detection rates would increase to 98.2%, but at false-positive rates of 24.2%. Assuming half of the spread in the error distribution, using the 10 th and 90 th percentiles to screen for SGA and LGA fetuses, respectively, the detection rates would be 86.6% at false-positive rates of 2.3%. If the cut-offs were relaxed to the 15 th and 85 th percentiles, respectively, the detection rates would increase to 97.0% and the false-positive rates would increase to 6.3%.Conclusions Measurement error in fetal biometry causes substantial error in EFW, resulting in misclassification of SGA and LGA fetuses. The extent to which improvement can be achieved through effective quality assurance remains to be seen but, as a first step, it is important for practitioners to understand how biometric measurement error impacts the prediction of SGA and LGA fetuses.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Wright

Smith³

et al. 2020

Ultrasound in Obstet & Gyne

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Deep learning to estimate gestational age from fly‐to cineloop videos: A novel approach to ultrasound quality control

Viswanathan,

Pokaprakarn,

Kasaro

et al. 2024

Intl J Gynecology & Obste

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ObjectiveLow‐cost devices have made obstetric sonography possible in settings where it was previously unfeasible, but ensuring quality and consistency at scale remains a challenge. In the present study, we sought to create a tool to reduce substandard fetal biometry measurement while minimizing care disruption.MethodsWe developed a deep learning artificial intelligence (AI) model to estimate gestational age (GA) in the second and third trimester from fly‐to cineloops—brief videos acquired during routine ultrasound biometry—and evaluated its performance in comparison to expert sonographer measurement. We then introduced random error into fetal biometry measurements and analyzed the ability of the AI model to flag grossly inaccurate measurements such as those that might be obtained by a novice.ResultsThe mean absolute error (MAE) of our model (±standard error) was 3.87 ± 0.07 days, compared to 4.80 ± 0.10 days for expert biometry (difference −0.92 days; 95% CI: −1.10 to −0.76). Based on simulated novice biometry with average absolute error of 7.5%, our model reliably detected cases where novice biometry differed from expert biometry by 10 days or more, with an area under the receiver operating characteristics curve of 0.93 (95% CI: 0.92, 0.95), sensitivity of 81.0% (95% CI: 77.9, 83.8), and specificity of 89.9% (95% CI: 88.1, 91.5). These results held across a range of sensitivity analyses, including where the model was provided suboptimal truncated fly‐to cineloops.ConclusionsOur AI model estimated GA more accurately than expert biometry. Because fly‐to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no‐cost guardrail that could be incorporated into both low‐cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors.

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The impact of individual sonographer variation on the detection of small for gestational age fetuses using a third trimester growth scan

Procas-Ramon

Hierro-Espinosa

Salim

et al. 2021

J of Clinical Ultrasound

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Objectives Detection of small for gestational age (SGA) fetuses in a third trimester ultrasound could be affected by variation in sonographer performance. Methods Retrospective analysis of all singleton, non‐anomalous ultrasound examinations between 35+0‐36+6 weeks gestation, in a single institution where a universal 36‐week scan is offered. Screen positive was defined as estimated fetal weight (EFW) <10th centile; SGA was birthweight <10th centile. Individual sonographers' distributions of head circumference (HC), abdominal circumference (AC) and femur length (FL) were used to assess sonographers' screen positive rate (SPR), detection rate (DR) and true positive rate (TPR). Univariate and multivariate regression analysis was performed to assess the association between the sonographers' mean and SD (SD) for HC, AC, FL and their SPR, DR and TPR. Results There were 27 sonographers performing more than 50 examinations per year, a total of 5691 scans. The mean incidence of SGA was 10.0%. For an overall SPR of 9.4%, the overall DR was 43.8% (95% CI: 39.6% ‐ 48.1%) and the overall TPR was 46.5% (95% CI: 42.9% ‐ 50.2%). Higher AC scatter (SD difference up to 11.6 mm) was associated with higher SPR (P = 0.001). Lower mean FL (difference up to 3.6 mm) was associated with higher SPR (P = 0.003) and higher DR (P = 0.002). As a result, DR varied amongst different sonographers between 14.3% and 85.7% and TPR varied between 8.3% and 100.0%. Conclusions Monitoring of individual AC and FL distributions is a simple and effective tool for institutional quality assurance.

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Quality control of ultrasound for fetal biometry: results from the INTERGROWTH‐21^st Project

Cited by 53 publications

References 51 publications

Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Deep learning to estimate gestational age from fly‐to cineloop videos: A novel approach to ultrasound quality control

The impact of individual sonographer variation on the detection of small for gestational age fetuses using a third trimester growth scan

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Quality control of ultrasound for fetal biometry: results from the INTERGROWTH‐21st Project

Cited by 53 publications

References 51 publications

Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Impact of biometric measurement error on identification of small‐ and large‐for‐gestational‐age fetuses

Deep learning to estimate gestational age from fly‐to cineloop videos: A novel approach to ultrasound quality control

The impact of individual sonographer variation on the detection of small for gestational age fetuses using a third trimester growth scan

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Quality control of ultrasound for fetal biometry: results from the INTERGROWTH‐21^st Project