Does the Use of Automated Fetal Biometry Improve Clinical Work Flow Efficiency?

Espinoza, Jimmy; Good, Sara V.; Russell, Evie; Lee, Wesley

doi:10.7863/jum.2013.32.5.847

Cited by 40 publications

(21 citation statements)

References 17 publications

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“…Espinoza et al . demonstrated that automated measurements reduced the number of steps used and time required to scan and could improve efficiency in workflow. The extra time could be used in communicating with the expectant mother, which could lead to a more positive experience.…”

Section: Discussionmentioning

confidence: 99%

“…Rahmatullah et al 9 , using semi-automated measurement of the femur after segmentation and extraction, and Zalud et al 10 , using a different algorithm, also showed that automated measurements were reliable and reproducible. Espinoza et al demonstrated that automated measurements reduced the number of steps used and time required to scan and could improve efficiency in workflow 4 . The extra time could be used in communicating with the expectant mother, which could lead to a more positive experience.…”

Section: Discussionmentioning

confidence: 99%

“…Automation of measurements during obstetric scanning is an attractive option to increase reliability and reproducibility of measurements, while also reducing scanning time 3,4 . A reduction of work-related musculoskeletal disorders secondary to repetitive movements would also be a desirable by-product of such a system 5,6 .…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of automated tool for two‐dimensional fetal biometry

Salim

Cavallaro

Ciofolo-Veit

et al. 2019

Ultrasound in Obstet & Gyne

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Objective To evaluate whether an automated tool can recognize a structure of interest and measure fetal head circumference (HC), abdominal circumference (AC) and femur length (FL) on frozen two‐dimensional ultrasound images. Methods Ultrasound examinations were performed in 100 singleton pregnancies between 20 and 40 weeks of gestation, ensuring an even distribution throughout gestational age. In each pregnancy, three standard biometric variables (HC, AC, FL) were measured each in three different images obtained for this purpose (i.e. nine independent image acquisitions). An algorithm (Philips Research) was used to detect the structure of interest and automatically place calipers for measurement. Caliper placement was assessed in two ways. First, subjective clinical assessment was performed to determine whether the caliper placement was correct, and caliper position was classified as ‘acceptable for clinical use’, ‘minor adjustment required’ or ‘major adjustment required’. Second, the resulting automatic measurements were compared with manual measurements, taken in real time. Mean difference errors were calculated and expressed as percentages to correct for fetal growth with advancing gestation. Results After exclusion of one pregnancy due to missing images, a total of 891 images (297 for each biometric variable) from 99 pregnancies were analyzed. The algorithm failed to place calipers for the AC in nine images, whereas there were no failures in caliper placement for measurement of HC and FL. On subjective quality assessment of automatic caliper placement, in 475 (53.3%) images position of the calipers was judged to be clinically acceptable and did not require any adjustment, while in 317 (35.6%) and 90 images (10.1%) minor and major adjustments were required, respectively. The mean error between manual and automatic measurement of HC was −0.21 cm corresponding to a percentage error of −0.81% with 95% limits of agreement (LOA) between −3.73% and 2.12%. For AC and FL measurements, the mean error was, respectively, 0.72 cm (percentage error, 2.40%) with LOA between −9.48% and 14.27%, and 0.21 cm (percentage error, 3.76%) with LOA between −8.38% and 15.91%. Conclusions The automated tool identified correctly the biometric variable of interest in 99% of frozen images. The resulting measurements had a high degree of accuracy and compared well with previously published manual‐to‐manual agreement. The measurements exhibited bias, with the automated tool underestimating biometry; this could be overcome by further improvements in the algorithm. Nevertheless, adjustable calipers for manual correction remains a requirement. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Evaluation of automated tool for two‐dimensional fetal biometry

Salim

Cavallaro

Ciofolo-Veit

et al. 2019

Ultrasound in Obstet & Gyne

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“…However, these factors are operator-dependent and time-consuming involving multiple keystrokes and probe motions. Hence, to improve the workflows and reduce user variability in data acquisition, there has been an increasing demand for automatic estimation of fetal biometric parameters [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic evaluation of fetal head biometry from ultrasound images using machine learning

Kim¹,

Lee

Kwon

et al. 2019

Physiol. Meas.

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Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing automated methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artifacts in ultrasound images. This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability. The proposed method effectively identifies the head boundary by differentiating tissue image patterns with respect to the ultrasound propagation direction. The proposed method was trained with 102 labeled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC and BPD estimations, and an accuracy of 87.14% for the plane acceptance check.Index Terms-ultrasound, fetal head biometry, machine learning arXiv:1808.06150v1 [physics.med-ph]

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“…To ensure a precise AC plane that is perpendicular to the true fetal longitudinal axis, clinician has to continuously move the transducer to find a plane consisting accurate landmarks. This process is firstly, cumbersome as fetal movement, breathing movement, and fetal position hinder prompt acquisition of the plane; and secondly, may lead to inaccurate measurement as inexperienced operators often fail to adhere to multiple landmarks of correct AC plane [6]. Therefore, development and implementation of automated fetal biometric measurements has recently gained spotlight in hope to improve clinicians' workflow and to overcome operator-dependency [1], [6].…”

Section: Introductionmentioning

confidence: 99%

Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images

Jang

Park

Kim

et al. 2018

IEEE J. Biomed. Health Inform.

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Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.

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Does the Use of Automated Fetal Biometry Improve Clinical Work Flow Efficiency?

Cited by 40 publications

References 17 publications

Evaluation of automated tool for two‐dimensional fetal biometry

Evaluation of automated tool for two‐dimensional fetal biometry

Automatic evaluation of fetal head biometry from ultrasound images using machine learning

Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images

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