ArticleOpen veterinary journal2026
Development and evaluation of a gelatin-based phantom model for veterinary diagnostic imaging training.
Article in Open veterinary journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Background: Simulation-based training enhances the competency of veterinarians and veterinary students in diagnostic imaging, with phantoms serving as ethical alternatives to the use of animal tissues. However, in many developing countries, the high cost of commercial phantoms limits their accessibility. Aim: This study aimed to develop and evaluate a low-cost, gelatin-based phantom as an alternative model for veterinary training. Methods: In this study, we developed and evaluated a low-cost phantom using animal-based gelatin in the form of "ballistic gel" with or without 10% buffered normal formaldehyde (BNF) preservative. Metal beads, plastic beads, and quail eggs were embedded within the phantom as foreign body models. Durability and imaging quality were assessed using ultrasonography (USG), digital radiography, and electrical impedance tomography (EIT). Results: The non-preserved phantom exhibited extensive syneresis (90.95%), whereas the BNF-preserved phantom exhibited minimal syneresis (2.53%), thereby maintaining a better texture and durability. The addition of the BNF preservative did not affect echogenicity or visibility of embedded foreign bodies. USG provides limited differentiation among foreign body types, whereas radiography offers a clear distinction between them. EIT further identifies foreign bodies through resistivity contrast, with materials represented by distinct colors. Conclusion: BNF-preserved gelatin phantoms offer a cost-effective and durable alternative for simulation-based imaging training.
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