Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video
Open accessAbstract
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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The PULSE project, presented by Drukker et al., was designed to enable insights into experts' sonography workflow and to transform the learning process of obstetric US using deep-learning algorithms.
“The PULSE (Perception Ultrasound by Learning Sonographer Experience) project, presented by Drukker et al., was designed to enable insights into experts’ sonography workflow and to transform the learning process of obstetric US using deep-learning algorithms [154].”
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 [154]
The Introduction and Discussion describe PULSE as a project that records expert sonographers’ actions and uses deep learning to study scanning workflow. The Abstract says it aims to transform ultrasound acquisition and image analysis, while the Discussion says the data may help researchers understand how to train sonographers and monitor learning. It does not describe transforming the learning process itself with deep-learning algorithms. (LLM, conf=0.86)
Experimental — accuracy not yet measuredDrukker et al. analyzed eye and transducer movements, actions during scanning, and audio recordings to generate automated image captioning of the sonographer's explanation.
“While Drukker et al. analyzed eye and transducer movements, actions during scanning, and audio recordings to generate automated image captioning of the sonographer’s explanation”
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 [154]
The Methods (Data analysis; page unavailable) describe analyses of transducer motion, eye tracking, scanning activity, and transcribed audio. The Results (Image captioning; page unavailable) report automatically generated captions modeled on sonographers’ language. The paper does not say that eye movements, transducer movements, or scanning actions were used to generate those captions; it links captioning to transcribed speech and ultrasound images. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI in scan workflow automated image labeling.
“the included studies on this topic showed clinical benefits of AI in scan workflow through automating image labeling [154,157]”
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 [154]
The Results (Workflow analysis; page unavailable) report that a deep neural network automatically labeled 279 obstetric anomaly scans for workflow analysis. The Discussion describes this as a clinically meaningful example, but presents improvements to training, auditing, and scan cost-effectiveness as potential benefits rather than demonstrated clinical outcomes. (LLM, conf=0.85)
Experimental — accuracy not yet measured
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Self-citationGleed, A. D., Mishra, D., Chandramohan, V., Fu, Z., Self, A., Bhatnagar, S., Papageorghiou, A. T., & Noble, J. A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230662 · Cited by 3 · see citing works
Self-citationWeakly Supervised Captioning of Ultrasound Images
Alsharid, M., Sharma, H., Drukker, L., Papageorgiou, A. T., & Noble, J. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12053-4_14 · Cited by 3 · see citing works
Self-citationDrukker, L. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27703 · Cited by 3 · see citing works
Self-citationSTAN-LOC: Visual Query-Based Video Clip Localization for Fetal Ultrasound Sweep Videos
Mishra, D., Saha, P., Zhao, H., Patey, O. V., Papageorghiou, A. T., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_69 · Cited by 2 · see citing works
Self-citationYasrab, R., Alsharid, M., Sarker, M. M. K., Zhao, H., Papageorghiou, A. T., & Noble, J. A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230422 · Cited by 2 · see citing works
Self-citationAudio-visual modelling in a clinical setting
Jiao, J., Alsharid, M., Drukker, L., Papageorghiou, A. T., Zisserman, A., & Noble, J. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-66160-4 · Cited by 2 · see citing works
Self-citationTeng, C., Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2022) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3517031.3529635 · Cited by 2 · see citing works
Self-citationFetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery
Zhang, K., Jiao, J., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73647-6_18 · Cited by 1 · see citing works
Self-citationZoom Pattern Signatures for Fetal Ultrasound Structures
Alsharid, M., Yasrab, R., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_73 · Cited by 1 · see citing works
Self-citationMachine learning-based method for the detection of dextrocardia in ultrasound video clips
Hernandez-Cruz, N., Patey, O. V., Šalović, B., Papageorghiou, A. T., & Noble, J. A. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109023 · Cited by 1 · see citing works
Self-citationReview of generative AI for lesion localization and automatic report generation
Sun, K., Wang, Y., Huang, S., Liu, Y., Chen, H., Lian, Z., Tan, M., Lin, X., Zhou, W., Qiu, Z., et al. (2026) · Med-X · doi:10.1007/s44258-026-00076-8 · Cited by 1 · see citing works
Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data.
Teng, C., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2023) · PubMed · Cited by 1 · see citing works
Self-citationAvilés, E., Romero, S. E., & Castañeda, B. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373436 · Cited by 1 · see citing works
Zoom is Meaningful: Discerning Ultrasound Images’ Zoom Levels
Alsharid, M., Yasrab, R., Sarker, M. M. K., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635854 · Cited by 1 · see citing works
Self-citationPose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Men, Q., Guo, X., Papageorghiou, A. T., & Noble, J. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_65 · Cited by 1 · see citing works
Self-citationCobotic Ultrasound Systems in Obstetric Assessment: A Scoping Review
Watson, C. A., Armstrong, L., Cook, D. M., Sazzad, T. M. S., & Parange, N. (2025) · Sonography · doi:10.1002/sono.12535 · Cited by 1 · see citing works
A picture is worth a thousand words: textual analysis of the routine 20‐week scan
Alsharid, M., Drukker, L., Sharma, H., Noble, J. A., & Papageorghiou, A. T. (2022) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.24972
Self-citationStellenwert der KI im pränatalen sonographischen Screening
Ritgen, J. (2022) · Die Gynäkologie · doi:10.1007/s00129-022-04995-6
Gwaba, N., Ngulube, M., & Hachinene, O. (2023) · South African Radiographer · doi:10.54450/saradio.2023.61.2.751
Lous, M. L., Vasconcelos, F., Vece, C. D., Dromey, B., Napolitano, R., Yoo, S., Edwards, E., Huaulmé, A., Peebles, D., Stoyanov, D. V., et al. (2024) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2024.04.042
An Automated Pipeline for the Identification of Liver Tissue in Ultrasound Video
Ockenden, E. S., Mpooya, S., Noble, J. A., & Chami, G. F. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-79103-1_10
Self-citationObstetric Ultrasound Utilization and Expenditures in a Commercially Insured Population (2016–2022)
Speller, A., Armstrong, J., Fox, K. P., & Sinaiko, A. D. (2025) · Journal of Ultrasound in Medicine · doi:10.1002/jum.70157
Wang, M.-T., Lin, Y.-C., Hsu, C.-H., Hsu, H., Yang, T., Kuo, L., & Wu, H.-W. (2026) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/09544119251414217
Matsumoto, H., Shirouchi, M., Chikako, H., Okada, K., Nakagawa, K., Tomii, N., Kinjo, T., & Yoshioka‐Maeda, K. (2026) · Journal of Radiology Nursing · doi:10.1016/j.jradnu.2026.101403
Ockenden, E. S., Anguajibi, V., Mpooya, S., Ntegeka, B., Mugume, T., Nabatte, B., Kabatereine, N. B., Noble, A., & Chami, G. F. (2026) · medRxiv · doi:10.64898/2026.06.01.26354609
Beyond image generation: visual data analysis with diffusion models—a comprehensive survey
Olechno, B., & Mańdziuk, J. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11615-5
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Fetal Ultrasound Screening Early Pregnancy
Pu, B., Yang, J., Wang, L., Tan, Y., He, G., Dong, X., Lin, Q., Guo, J., Yang, L., Liu, Z., et al. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817453
Sensor-based Knowledge Graph Modeling of Cognitive Pathways in Brand Information Recognition
(75607), L. L., Wang, Z., & Zhang, X. (2026) · Sensors and Materials · doi:10.18494/sam6421
FOCUS: Towards Fetal Obstetric Corrective UltraSound Guidance
Lamdouar, H., Wang, A. F., Guo, X., Men, Q., Lander, J., Papageorghiou, A. T., & Noble, J. A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38236-8_19
Self-citation
- Authors
- Drukker, L., Sharma, H., Droste, R., Alsharid, M., Chatelain, P., Noble, J. A., & Papageorghiou, A. T.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Noble, J. Alison
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-07-08
- Volume
- 11
- Issue
- 1
- Pages
- 14109-14109
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8266837
- OpenAlex
- W3178709181
- Cited by
- 59
- References
- 48
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 12 · 2023: 13 · 2022: 15 · 2021: 1
- FWCI
- 7.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Research Council (ERC-ADG-2015 694581)
- NIHR Oxford Biomedical Research Centre
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Keywords
- prenatal ultrasound, sonographer training, sonographer experience, eye tracking, voice recording, ultrasound video, deep learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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