A Machine Learning Method for Automated Description and Workflow Analysis of First Trimester Ultrasound Scans

Abstract

Obstetric ultrasound assessment of fetal anatomy in the first trimester of pregnancy is one of the less explored fields in obstetric sonography because of the paucity of guidelines on anatomical screening and availability of data. This paper, for the first time, examines imaging proficiency and practices of first trimester ultrasound scanning through analysis of full-length ultrasound video scans. Findings from this study provide insights to inform the development of more effective user-machine interfaces, of targeted assistive technologies, as well as improvements in workflow protocols for first trimester scanning. Specifically, this paper presents an automated framework to model operator clinical workflow from full-length routine first-trimester fetal ultrasound scan videos. The 2D+t convolutional neural network-based architecture proposed for video annotation incorporates transfer learning and spatio-temporal (2D+t) modelling to automatically partition an ultrasound video into semantically meaningful temporal segments based on the fetal anatomy detected in the video. The model results in a cross-validation A1 accuracy of 96.10%, F1 = 0.95, precision = 0.94 and recall = 0.95. Automated semantic partitioning of unlabelled video scans (n=250) achieves a high correlation with expert annotations(ρ = 0.95, p = 0.06). Clinical workflow patterns, operator skill and its variability can be derived from the resulting representation using the detected anatomy labels, order, and distribution. It is shown that nuchal translucency (NT) is the toughest standard plane to acquire and most operators struggle to localize high-quality frames. Furthermore, it is found that newly qualified operators spend 25.56% more time on key biometry tasks than experienced operators.

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“…The inherent complexity of the task and the requirement for large datasets of rare fetal conditions pose significant challenges. For instance, [76][77][78][79][80][81] tested CNN's ability to detect brain abnormalities using 225 fetal MRIs of varying gestational ages that are publicly available. This resulted in a 95% accuracy rate in identifying pathologies such as polymicrogyria, agenesis of the corpus callosum, colpocephaly, and Dandy-Walker spectrum malformations among other abnormal fetal brains.…”

Application and Progress of Artificial Intelligence in Fetal Ultrasound

“…These challenges include the low signal-to-noise ratio of ultrasound images, the short fetal parietalrump length, and the mobility of the fetus in early gestation. Unskilled sonographers spend 25.56% more time on crucial biometric tasks than experts [38].…”

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

“…First-trimester US imaging is typically performed between 11 and 13 weeks of gestation [77]. Its primary uses are to confirm pregnancy viability, determine gestational age, evaluate multiple gestations, and screen for significant fetal anomalies such as neural tube defects, abdominal wall defects, cardiac anomalies, nuchal translucency (NT), and some significant fetal brain abnormalities [78,79].…”