ArticleProceedings. IEEE International Symposium on Biomedical Imaging2021
Multi-Modal Learning from Video, Eye Tracking, and Pupillometry for Operator Skill Characterization in Clinical Fetal Ultrasound.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A visually grounded language model for fetal ultrasound understanding.Nature biomedical engineering · 2026Article
- The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review.IEEE journal of biomedical and health informatics · 2024Review
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Skill, or Style? Classification of Fetal Sonography Eye-Tracking Data.Proceedings of machine learning research · 2023Article
- Task model-specific operator skill assessment in routine fetal ultrasound scanning.International journal of computer assisted radiology and surgery · 2022Article
- Skill Characterisation of Sonographer Gaze Patterns during Second Trimester Clinical Fetal Ultrasounds using Time Curves.Proceedings. Eye Tracking Research & Applications Symposium · 2022Article
- Visualising Spatio-Temporal Gaze Characteristics for Exploratory Data Analysis in Clinical Fetal Ultrasound Scans.Proceedings. Eye Tracking Research & Applications Symposium · 2022Article
- Towards Scale and Position Invariant Task Classification using Normalised Visual Scanpaths in Clinical Fetal Ultrasound.Simplifying medical ultrasound : second international workshop, ASMUS 2021 : held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021 : proceedings. ASMUS (Workshop) (2nd : 2021 : Online) · 2021Article
- Machine learning-based analysis of operator pupillary response to assess cognitive workload in clinical ultrasound imaging.Computers in biology and medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This paper presents a novel multi-modal learning approach for automated skill characterization of obstetric ultrasound operators using heterogeneous spatio-temporal sensory cues, namely, scan video, eye-tracking data, and pupillometric data, acquired in the clinical environment. We address pertinent challenges such as combining heterogeneous, small-scale and variable-length sequential datasets, to learn deep convolutional neural networks in real-world scenarios. We propose spatial encoding for multi-modal analysis using sonography standard plane images, spatial gaze maps, gaze trajectory images, and pupillary response images. We present and compare five multi-modal learning network architectures using late, intermediate, hybrid, and tensor fusion. We build models for the Heart and the Brain scanning tasks, and performance evaluation suggests that multi-modal learning networks outperform uni-modal networks, with the best-performing model achieving accuracies of 82.4% (Brain task) and 76.4% (Heart task) for the operator skill classification problem.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.