A training tool for clinicians in segmenting medical images to make 3D models.


Journal

Annals of surgery open : perspectives of surgical history, education, and clinical approaches
ISSN: 2691-3593
Titre abrégé: Ann Surg Open
Pays: United States
ID NLM: 101769928

Informations de publication

Date de publication:
23 May 2023
Historique:
medline: 21 6 2023
pubmed: 21 6 2023
entrez: 21 6 2023
Statut: epublish

Résumé

3D models produced from medical imaging can be used to plan treatment, design prosthesis, teach and for communication. Despite the clinical benefit, few clinicians have experience of how 3D models are produced.This is the first study evaluating a training tool to teach clinicians to produce 3D models and reporting the perceived impact on their clinical practice. Following ethical approval, 10 clinicians completed a bespoke training tool, comprising written and video material alongside online support. Each clinician and 2 technicians (included as control) were sent 3 CT scans and asked to produce 6 fibula 3D models using an open-source software (3Dslicer). The produced models were compared to those produced by the technicians using Hausdorff distance calculation. Thematic analysis was used to study the post-intervention questionnaire. The mean Hausdorff distance between the final model produced by the clinicians and technicians was 0.65mm SD0.54mm. The first model made by clinicians took a mean time of 1hr 25mins and the final model took 16:04mins (5:00-46:00mins). 100% of learners reported finding the training tool useful and will employ it in future practice. The training tool described in this paper is able to successfully train clinicians to produce fibula models from CT scans. Learners were able to produce comparable models to technicians within an acceptable timeframe. This does not replace technicians. However, the learners perceived this training will allow them to use this technology in more cases, with appropriate case selection and they appreciate the limits of this technology.

Identifiants

pubmed: 37342255
doi: 10.1097/AS9.0000000000000275
pmc: PMC7614675
mid: EMS170894
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e275

Subventions

Organisme : Wellcome Trust
ID : 203145
Pays : United Kingdom

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Auteurs

Soudeh Chegini (S)

Department of Head and Neck Surgery, University College London Hospital, Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London.

Arpan Tahim (A)

OMFS specialist training, London Deanery.

Mingjun Liu (M)

Department of Medical Physics, University College London Hospital.

Yean Chooi (Y)

Department of Craniofacial Surgery, Great Ormond Street Hospital.

Eddie Edwards (E)

Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London.

Matthew Clarkson (M)

Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London.

Clare Schilling (C)

Head and Neck Academic Centre, University College London.

Classifications MeSH