The potential of 3D models and augmented reality in teaching cross-sectional radiology.

3D segmentation anatomic structures augmented reality medical education medical imaging

Journal

Medical teacher
ISSN: 1466-187X
Titre abrégé: Med Teach
Pays: England
ID NLM: 7909593

Informations de publication

Date de publication:
Oct 2023
Historique:
medline: 26 9 2023
pubmed: 5 8 2023
entrez: 4 8 2023
Statut: ppublish

Résumé

What was the educational challenge?The complexity and variability of cross-sectional imaging present a significant challenge in imparting knowledge of radiologic anatomy to medical students.What was the solution?Recent advancements in three-dimensional (3D) segmentation and augmented reality (AR) technology provide a promising solution. These advances allow for the creation of interactive, patient-specific 3D/AR models which incorporate multiple imaging modalities including MRI, CT, and 3D rotational angiography can help trainees understand cross-sectional imaging.How was the solution implemented?To create the model, DICOM files of patient scans with slice thicknesses of 1 mm or less are exported to a computer and imported to 3D Slicer for registration. Once registered, the files are segmented with Vitrea software utilizing thresholding, region growing, and edge detection. After the creation of the models, they are then imported to a web-based interactive viewing platform and/or AR application.What lessons were learned that are relevant to a wider global audience?Low-resource 3D/AR models offer an accessible and intuitive tool to teach radiologic anatomy and pathology. Our novel method of creating these models leverages recent advances in 3D/AR technology to create a better experience than traditional high and low-resource 3D/AR modeling techniques. This will allow trainees to better understand cross-sectional imaging.What are the next steps?The interactive and intuitive nature of 3D and AR models has the potential to significantly improve the teaching and presentation of radiologic anatomy and pathology to a medical student audience. We encourage educators to incorporate 3D segmentation models and AR in their teaching strategies.

Identifiants

pubmed: 37542360
doi: 10.1080/0142159X.2023.2242170
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1108-1111

Auteurs

Benjamin M Pinsky (BM)

University of Michigan Medical School, University of Michigan, Ann Arbor, USA.

Sreehari Panicker (S)

University of Michigan Medical School, University of Michigan, Ann Arbor, USA.

Neeraj Chaudhary (N)

University of Michigan Medical School, University of Michigan, Ann Arbor, USA.
Department of Radiology, University of Michigan, Ann Arbor, USA.
Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
Department of Neurology, University of Michigan, Ann Arbor, USA.
Department of Otorhinolaryngology, University of Michigan, Ann Arbor, USA.

Joseph J Gemmete (JJ)

University of Michigan Medical School, University of Michigan, Ann Arbor, USA.
Department of Radiology, University of Michigan, Ann Arbor, USA.
Department of Neurosurgery, University of Michigan, Ann Arbor, USA.
Department of Neurology, University of Michigan, Ann Arbor, USA.
Department of Otorhinolaryngology, University of Michigan, Ann Arbor, USA.

Zachary M Wilseck (ZM)

Department of Radiology, University of Michigan, Ann Arbor, USA.

Leanne Lin (L)

Department of Radiology, University of Michigan, Ann Arbor, USA.

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