Visual Augmentations for Ultrasound Assessment Training of Medical Students
Helena Bøjer Djernæs, Rune Møberg Jacobsen, Simo Hosio, Niels van Berkel
Abstract
Ultrasound assessments are key in assessing traumatic injuries to the human body during urgent medical emergencies. Obtaining profciency in conducting ultrasound assessments is challenging, and relies on hands-on, individually instructed training provided by a scarce number of ultrasound experts. We investigate how to support medical students' learning of ultrasound assessment through visual augmentations. By enhancing the learning process, we seek to support medical students in reaching higher profciency in ultrasound assessments. We followed an ultrasound assessment course to identify the primary challenges faced by medical students learning to conduct ultrasound assessments. Based on our fndings, we designed four distinct visual augmentations in collaboration with a course educator that guide students in achieving better ultrasound image quality. We evaluated these visual augmentations in a mixed-method study with 15 medical students. Our fndings provide insights on the use of digital technology in supporting clinical training, and the possibilities of bridging existing training practices.
• Human-centered computing → Human computer interaction (HCI); HCI design and evaluation methods; • Applied computing → Health informatics.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on7
- ARTEMIS: A Collaborative Mixed-Reality System for Immersive Surgical TelementoringDanilo Gasques, Janet G. Johnson, Tommy Sharkey, Yuanyuan Feng et al.CHI 2021 · 144 citations
- CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging AnalysisYao Xie, Melody Chen, David Kao, Ge Gao et al.CHI 2020 · 137 citations
- Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support SystemsCecilia Panigutti, Andrea Beretta, Fosca Giannotti, Dino PedreschiCHI 2022 · 129 citations
- "If I Had All the Time in the World": Ophthalmologists' Perceptions of Anchoring Bias Mitigation in Clinical AI SupportAnne Kathrine Petersen Bach, Trine Munch Nørgaard, Jens Christian Brok, Niels van BerkelCHI 2023 · 46 citations
- Tangible Immersive Trauma Simulation: Is Mixed Reality the next level of medical skills training?Jakob Carl Uhl, Helmut Schrom-Feiertag, Georg Regal, Katja Gallhuber et al.CHI 2023 · 45 citations
Related papers
- MRUCT: Mixed Reality Assistance for Acupuncture Guided by Ultrasonic Computed TomographyXinkai Wang, Yue Yang, Kehong Zhou, Xue Xie et al.IEEE VR 2025 · 3 citations
- ESSA: Explanation Iterative Supervision via Saliency-guided Data AugmentationSiyi Gu, Yifei Zhang, Yuyang Gao, Xiaofeng Yang et al.KDD 2023 · 7 citations
- SHands: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical TrainingLe Ma, Thiago Freitas dos Santos, Nadia Magnenat-Thalmann, Katarzyna WacCVPR 2026
- Should the AI Speak First? Evaluating Proactive vs. Reactive Facilitation in Mixed-Reality Medical TrainingDuo Wang, Wenxuan Song, Jianwei Ni, Qingxiao Zheng et al.CHI 2026
- FETAL-GAUGE: A BENCHMARK FOR ASSESSING VISION-LANGUAGE MODELS IN FETAL ULTRASOUNDHussain Alasmawi, Numan Saeed, Mohammad YaqubICLR 2026
