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Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize not only the current subtasks but also the future ones that are causally related. We investigate the impact of visualizing causality within an MR framework on manual task skill learning. We conducted a user study with 48 participants, experimenting with how presenting tasks in hierarchical causality levels (no causality, event-level, interaction-level, and gesture-level causality) affects user comprehension and performance in a complex assembly task. The research finds that displaying all causality levels enhances user understanding and task execution, with a compromise of learning time. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems.
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http://dx.doi.org/10.1109/TVCG.2025.3542949 | DOI Listing |
IEEE Trans Autom Sci Eng
January 2025
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Cone beam computed tomography (CBCT) is a widely-used imaging modality in dental healthcare. It is an important task to segment each 3D CBCT image, which involves labeling lesions, bone, teeth, and restorative material on a voxel-by-voxel basis, as it aids in lesion detection, diagnosis, and treatment planning. The current clinical practice relies on manual segmentation, which is labor-intensive and demands considerable expertise.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.
View Article and Find Full Text PDFJ Am Coll Cardiol
August 2025
Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address:
Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.
Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.
Med Biol Eng Comput
September 2025
College of Medicine and Biomedical Information Engineering, Northeastern University, 110169, Shenyang, China.
Recognition of tumors is very important in clinical practice and radiomics; however, the segmentation task currently still needs to be done manually by experts. With the development of deep learning, automatic segmentation of tumors is gradually becoming possible. This paper combines the molecular information from PET and the pathology information from CT for tumor segmentation.
View Article and Find Full Text PDFJ Robot Surg
September 2025
Department of Anesthesiology and Critical Care, Houston Methodist, 6565 Fannin St, B452, Houston, TX, 77030, USA.
Defining performance errors in robotic surgery is critical for the assessment of robotic surgery skill. Our goal was to identify and categorize explicitly defined intraoperative technical errors in robotic surgery, how skill assessment was performed, and how ratings were conducted either manually by experts or via automated ratings. This scoping review included studies involving general, urologic, obstetrics/gynecologic, and thoracic surgery, and general skills as practiced in inanimate, virtual reality, in vivo/ex vivo animal, cadaver, and human operations.
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