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

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 prac...

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Bibliographic Details
Published in:	IEEE transactions on medical imaging Vol. 42; no. 5; p. 1
Main Authors:	Yasrab, Robail, Fu, Zeyu, Zhao, He, Lee, Lok Hin, Sharma, Harshita, Drukker, Lior, Papageorgiou, Aris T, Alison Noble, J.
Format:	Journal Article
Language:	English
Published:	United States IEEE 01-05-2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anatomy Annotations Artificial neural networks Automation clinical workflow Female Fetuses first trimester Humans Image annotation Machine Learning Neural networks Nuchal Translucency Measurement - methods Obstetrics Operators Pregnancy Pregnancy Trimester, First Scanning spatio-temporal analysis Streaming media Task analysis Transfer learning Two dimensional models Ultrasonic imaging Ultrasonic variables measurement Ultrasonography, Prenatal - methods Ultrasound video classification Workflow
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0278-0062 1558-254X
DOI:	10.1109/TMI.2022.3226274