Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos
Open accessAbstract
Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks.
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Sharma et al. added pupillometric data to objectify the localization of the sonographer's gaze on the screen and the intensity of concentration in this focus.
“Sharma et al. added the pupillometric data to objectify not only the localization of the sonographer’s gaze on the screen, but also the intensity of concentration in this focus [155].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [155]
Section 2.1 says the study analysed clinical workflow solely from routine ultrasound scan videos. The Validation of Manual Annotations section proposes eye-tracking data as a possible future way to determine where an operator is looking; it does not describe adding pupillometric data or measuring concentration. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredA limitation in workflow analysis applications was the impossibility of the generalization of workflow sequences due to maternal−fetal factors and different skill levels of sonographers for different anatomical regions during a full routine scan.
“Limitations in application were reported to be the impossibility of the generalization of workflow sequences due to maternal−fetal factors and different skill levels of sonographers for different anatomical regions during a full routine scan [155,158].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [155]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAI in scan workflow optimized workflow.
“and optimizing workflow [155].”
From Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology (2023) · cites this paper as [155]
The Introduction (page unavailable) identifies increasing operator efficiency as a potential use of workflow analysis. The Discussion and conclusion (page unavailable) describes the methods as a proof of concept that may lead to efficient workflow management systems; it does not report that the study optimized scan workflow. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSharma et al. and Drukker et al. analyzed full-length scanning videos to assess workflow by building timeline models illustrating the scanning sequence of anatomical regions over time.
“Sharma et al. and Drukker et al. analyzed full-length scanning videos to assess workflow by building timeline models illustrating the scanning sequence of anatomical regions over time [155,158].”
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Self-citationSharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433863 · Cited by 15 · see citing works
Self-citation
- Authors
- Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Sharma, Harshita
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 69
- Pages
- 101973
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W3121390535
- Cited by
- 48
- References
- 76
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 12 · 2021: 6
- FWCI
- 7.99
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Image Analysis (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- NIHR Oxford Biomedical Research Centre
- European Research Council
- Topics
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- prenatal ultrasound, ultrasound video analysis, video semantic segmentation, spatiotemporal deep learning, knowledge representation, operator variability, ultrasound training
- MeSH terms
- Computer Simulation, Female, Humans, Pregnancy, Retrospective Studies, Ultrasonography, Prenatal, Neural Networks, Computer, Workflow
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