Development of 3D Training Models for the Identification and Classification of Colorectal Polyps

Roberts, A, O'Toole, P, Roughley, M and Rankin, M (2025) Development of 3D Training Models for the Identification and Classification of Colorectal Polyps. Journal of Visual Communication in Medicine. ISSN 1745-3054

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Abstract

Colorectal cancers develop from pre-malignant polyps that can be removed during colonoscopy. Detection, assessment, and removal of polyps has a major role in bowel cancer prevention and is an important part of bowel cancer screening programmes. Trainee colonoscopists must acquire skills to recognise and classify colorectal polyps. Accurate classification is based on morphology, surface pit and capillary patterns. It is difficult to teach assessment skills because static polyp images are often of poor quality and cannot show all areas of interest. Based on anonymised, endoscopic reference images, 3D polyp models were created in ZBrush, demonstrating a variety of morphological forms. The models had detailed pit patterns to show the capillary structure, a key predictor of pathology. The models were subsequently uploaded to the online 3D repository and model viewer, Sketchfab, to create an interactive training resource for trainee colonoscopists.The digital models were evaluated by a panel of expert colonoscopists who scored them for realism and potential as aids for training. There was agreement that the digital polyp models would be useful for teaching. Polyp morphology was rated as realistic however representation of pit patterns received a mixed response, highlighting areas for further development.

Item Type:	Article
Uncontrolled Keywords:	4203 Health services and systems; 4601 Applied computing; 4609 Information systems
Subjects:	N Fine Arts > N Visual arts (General) For photography, see TR R Medicine > R Medicine (General)
Divisions:	Art and Design
Publisher:	Taylor and Francis Group
Date of acceptance:	7 March 2025
Date of first compliant Open Access:	22 April 2025
Date Deposited:	10 Mar 2025 16:32
Last Modified:	22 Apr 2025 15:00
DOI or ID number:	10.1080/17453054.2025.2485956
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25822

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