OP01.10: Auditing the quality of ultrasound images using an AI solution: ScanNav® for fetal second trimester ultrasound scans

Yaqub, Mohammad; Cook, Katy; Cocks, Kim; Chen, Z.; Chikkanna, B.; Sleep, Nicholas; Noble, Alison; Papageorghiou, A.T.

doi:10.1002/uog.20656

Cited by 4 publications

(1 citation statement)

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“…ScanNav ® (Intelligent Ultrasound Ltd., Milton Park, Abingdon, United Kingdom) 18 is a precommercial deep learning-based automatic image analysis software application that assesses the quality of second-trimester scans according to the UK FASP guidelines. It is able to accurately detect different structures of a standard plane in large scale.…”

Section: Quantitative Metrics For Image Simulation Qualitymentioning

confidence: 99%

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation

Lee

Drukker

et al. 2020

J. Med. Imag.

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Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data as pairwise data were not available. We also report original insights on the general appearance differences between second-and third-trimester fetal head transventricular (TV) plane images.Approach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second-and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deeplearning-based image recognition algorithm (ScanNav ® ) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently.Results: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second-and real third-trimester scans was computed by ScanNav ® and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method. Conclusions:The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav ® ) for large-scale quantitative image analysis evaluation is also a first to our knowledge.

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Section: Quantitative Metrics For Image Simulation Qualitymentioning

confidence: 99%

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation

Lee

Drukker

et al. 2020

J. Med. Imag.

View full text Add to dashboard Cite

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Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

Drukker

Sharma

Droste

et al. 2021

Sci Rep

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Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer’s eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.

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Machine learning for accurate estimation of fetal gestational age based on ultrasound images

et al. 2023

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Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.

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OP01.10: Auditing the quality of ultrasound images using an AI solution: ScanNav® for fetal second trimester ultrasound scans

Cited by 4 publications

References 0 publications

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation

Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video

Machine learning for accurate estimation of fetal gestational age based on ultrasound images

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